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Update all references to canonical models (#29001)
* Script & Manual edition * Update
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README.md
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README.md
@ -89,13 +89,13 @@ You can test most of our models directly on their pages from the [model hub](htt
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Here are a few examples:
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In Natural Language Processing:
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- [Masked word completion with BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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- [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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- [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
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- [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
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- [Natural Language Inference with RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
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- [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
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- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
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- [Question answering with DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
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- [Translation with T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
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- [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
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- [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
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In Computer Vision:
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- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224)
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@ -201,8 +201,8 @@ In addition to `pipeline`, to download and use any of the pretrained models on y
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```python
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>>> from transformers import AutoTokenizer, AutoModel
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>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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>>> model = AutoModel.from_pretrained("bert-base-uncased")
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>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
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>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
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>>> inputs = tokenizer("Hello world!", return_tensors="pt")
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>>> outputs = model(**inputs)
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@ -212,8 +212,8 @@ And here is the equivalent code for TensorFlow:
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```python
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>>> from transformers import AutoTokenizer, TFAutoModel
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>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
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>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
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>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
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>>> inputs = tokenizer("Hello world!", return_tensors="tf")
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>>> outputs = model(**inputs)
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README_de.md
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README_de.md
@ -90,13 +90,13 @@ Hier sind einige Beispiele:
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In der Computerlinguistik:
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- [Maskierte Wortvervollständigung mit BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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- [Maskierte Wortvervollständigung mit BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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- [Eigennamenerkennung mit Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
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- [Textgenerierung mit GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
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- [Natural Language Inference mit RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
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- [Textgenerierung mit GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
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- [Natural Language Inference mit RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
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- [Automatische Textzusammenfassung mit BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
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- [Question Answering mit DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
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- [Maschinelle Übersetzung mit T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
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- [Question Answering mit DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
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- [Maschinelle Übersetzung mit T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
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In der Computer Vision:
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@ -197,8 +197,8 @@ Zusätzlich zur `pipeline` benötigt es nur drei Zeilen Code, um eines der vortr
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```python
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>>> from transformers import AutoTokenizer, AutoModel
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>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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>>> model = AutoModel.from_pretrained("bert-base-uncased")
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>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
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>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
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>>> inputs = tokenizer("Hello world!", return_tensors="pt")
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>>> outputs = model(**inputs)
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@ -209,8 +209,8 @@ Und hier ist der entsprechende Code für TensorFlow:
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```python
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>>> from transformers import AutoTokenizer, TFAutoModel
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>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
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>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
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>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
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>>> inputs = tokenizer("Hello world!", return_tensors="tf")
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>>> outputs = model(**inputs)
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README_es.md
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README_es.md
@ -84,13 +84,13 @@ Puedes probar la mayoría de nuestros modelos directamente en sus páginas desde
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Aquí hay algunos ejemplos:
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En procesamiento del lenguaje natural:
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- [Terminación de palabras enmascaradas con BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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- [Terminación de palabras enmascaradas con BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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- [Reconocimiento del nombre de la entidad con Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
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- [Generación de texto con GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
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- [Inferencia del lenguaje natural con RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
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- [Generación de texto con GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
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- [Inferencia del lenguaje natural con RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
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- [Resumen con BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
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- [Responder a preguntas con DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
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- [Traducción con T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
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- [Responder a preguntas con DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
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- [Traducción con T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
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En visión de ordenador:
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- [Clasificación de imágenes con ViT](https://huggingface.co/google/vit-base-patch16-224)
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@ -174,8 +174,8 @@ Además de `pipeline`, para descargar y usar cualquiera de los modelos previamen
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```python
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>>> from transformers import AutoTokenizer, AutoModel
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>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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>>> model = AutoModel.from_pretrained("bert-base-uncased")
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>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
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>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
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>>> inputs = tokenizer("Hello world!", return_tensors="pt")
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>>> outputs = model(**inputs)
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@ -185,8 +185,8 @@ Y aquí está el código equivalente para TensorFlow:
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```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
18
README_fr.md
18
README_fr.md
@ -89,13 +89,13 @@ Vous pouvez tester la plupart de nos modèles directement sur leurs pages du [hu
|
||||
Voici quelques exemples :
|
||||
|
||||
En traitement du langage naturel :
|
||||
- [Complétion de mots masqués avec BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Complétion de mots masqués avec BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Reconnaissance d'entités nommées avec Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [Génération de texte avec GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [Inférence de langage naturel avec RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [Génération de texte avec GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [Inférence de langage naturel avec RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [Résumé avec BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [Réponse aux questions avec DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [Traduction avec T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [Réponse aux questions avec DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [Traduction avec T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
En vision par ordinateur :
|
||||
- [Classification d'images avec ViT](https://huggingface.co/google/vit-base-patch16-224)
|
||||
@ -194,8 +194,8 @@ En plus de `pipeline`, pour télécharger et utiliser n'importe lequel des modè
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
inputs = tokenizer("Bonjour le monde !", return_tensors="pt")
|
||||
outputs = model(**inputs)
|
||||
@ -206,8 +206,8 @@ Et voici le code équivalent pour TensorFlow :
|
||||
```python
|
||||
from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
inputs = tokenizer("Bonjour le monde !", return_tensors="tf")
|
||||
outputs = model(**inputs)
|
||||
|
||||
18
README_hd.md
18
README_hd.md
@ -99,13 +99,13 @@ checkpoint: जाँच बिंदु
|
||||
आप सबसे सीधे मॉडल पृष्ठ पर परीक्षण कर सकते हैं [model hub](https://huggingface.co/models) मॉडल पर। हम [निजी मॉडल होस्टिंग, मॉडल संस्करण, और अनुमान एपीआई](https://huggingface.co/pricing) भी प्रदान करते हैं।。
|
||||
|
||||
यहाँ कुछ उदाहरण हैं：
|
||||
- [शब्द को भरने के लिए मास्क के रूप में BERT का प्रयोग करें](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [शब्द को भरने के लिए मास्क के रूप में BERT का प्रयोग करें](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [इलेक्ट्रा के साथ नामित इकाई पहचान](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [जीपीटी-2 के साथ टेक्स्ट जनरेशन](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [रॉबर्टा के साथ प्राकृतिक भाषा निष्कर्ष](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [जीपीटी-2 के साथ टेक्स्ट जनरेशन](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [रॉबर्टा के साथ प्राकृतिक भाषा निष्कर्ष](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [बार्ट के साथ पाठ सारांश](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [डिस्टिलबर्ट के साथ प्रश्नोत्तर](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [अनुवाद के लिए T5 का प्रयोग करें](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [डिस्टिलबर्ट के साथ प्रश्नोत्तर](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [अनुवाद के लिए T5 का प्रयोग करें](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
**[Write With Transformer](https://transformer.huggingface.co)**，हगिंग फेस टीम द्वारा बनाया गया, यह एक आधिकारिक पाठ पीढ़ी है demo。
|
||||
|
||||
@ -151,8 +151,8 @@ checkpoint: जाँच बिंदु
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -161,8 +161,8 @@ checkpoint: जाँच बिंदु
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
18
README_ja.md
18
README_ja.md
@ -119,13 +119,13 @@ user: ユーザ
|
||||
以下はその一例です:
|
||||
|
||||
自然言語処理にて:
|
||||
- [BERTによるマスクドワード補完](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [BERTによるマスクドワード補完](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Electraによる名前実体認識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [GPT-2によるテキスト生成](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [RoBERTaによる自然言語推論](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [GPT-2によるテキスト生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [RoBERTaによる自然言語推論](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [BARTによる要約](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [DistilBERTによる質問応答](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [T5による翻訳](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [DistilBERTによる質問応答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [T5による翻訳](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
コンピュータビジョンにて:
|
||||
- [ViTによる画像分類](https://huggingface.co/google/vit-base-patch16-224)
|
||||
@ -208,8 +208,8 @@ Hugging Faceチームによって作られた **[トランスフォーマーを
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -219,8 +219,8 @@ Hugging Faceチームによって作られた **[トランスフォーマーを
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
18
README_ko.md
18
README_ko.md
@ -74,13 +74,13 @@ limitations under the License.
|
||||
대부분의 모델을 [모델 허브](https://huggingface.co/models) 페이지에서 바로 테스트해볼 수 있습니다. 공개 및 비공개 모델을 위한 [비공개 모델 호스팅, 버전 관리, 추론 API](https://huggingface.co/pricing)도 제공합니다.
|
||||
|
||||
예시:
|
||||
- [BERT로 마스킹된 단어 완성하기](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [BERT로 마스킹된 단어 완성하기](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Electra를 이용한 개체명 인식](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [GPT-2로 텍스트 생성하기](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [RoBERTa로 자연어 추론하기](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [GPT-2로 텍스트 생성하기](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [RoBERTa로 자연어 추론하기](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [BART를 이용한 요약](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [T5로 번역하기](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [T5로 번역하기](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
**[Transformer와 글쓰기](https://transformer.huggingface.co)** 는 이 저장소의 텍스트 생성 능력에 관한 Hugging Face 팀의 공식 데모입니다.
|
||||
|
||||
@ -126,8 +126,8 @@ limitations under the License.
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -136,8 +136,8 @@ limitations under the License.
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
@ -93,13 +93,13 @@ Aqui estão alguns exemplos:
|
||||
|
||||
Em Processamento de Linguagem Natural:
|
||||
|
||||
- [Completar palavra mascarada com BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Completar palavra mascarada com BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Reconhecimento de Entidades Nomeadas com Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [Geração de texto com GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C)
|
||||
- [Inferência de Linguagem Natural com RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [Geração de texto com GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C)
|
||||
- [Inferência de Linguagem Natural com RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [Sumarização com BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [Resposta a perguntas com DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [Tradução com T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [Resposta a perguntas com DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [Tradução com T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
|
||||
Em Visão Computacional:
|
||||
@ -204,8 +204,8 @@ Além do `pipeline`, para baixar e usar qualquer um dos modelos pré-treinados e
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -216,8 +216,8 @@ E aqui está o código equivalente para TensorFlow:
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
18
README_ru.md
18
README_ru.md
@ -89,13 +89,13 @@ limitations under the License.
|
||||
Вот несколько примеров:
|
||||
|
||||
В области NLP ( Обработка текстов на естественном языке ):
|
||||
- [Маскированное заполнение слов с помощью BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Маскированное заполнение слов с помощью BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Распознавание сущностей с помощью Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [Генерация текста с помощью GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [Выводы на естественном языке с помощью RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [Генерация текста с помощью GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [Выводы на естественном языке с помощью RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [Обобщение с помощью BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [Ответы на вопросы с помощью DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [Перевод с помощью T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [Ответы на вопросы с помощью DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [Перевод с помощью T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
В области компьютерного зрения:
|
||||
- [Классификация изображений с помощью ViT](https://huggingface.co/google/vit-base-patch16-224)
|
||||
@ -196,8 +196,8 @@ Hugging Face Hub. Мы хотим, чтобы Transformers позволил ра
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Привет мир!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -207,8 +207,8 @@ Hugging Face Hub. Мы хотим, чтобы Transformers позволил ра
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Привет мир!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
18
README_te.md
18
README_te.md
@ -91,13 +91,13 @@ limitations under the License.
|
||||
ఇక్కడ కొన్ని ఉదాహరణలు ఉన్నాయి:
|
||||
|
||||
సహజ భాషా ప్రాసెసింగ్లో:
|
||||
- [BERT తో మాస్క్డ్ వర్డ్ కంప్లీషన్](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [BERT తో మాస్క్డ్ వర్డ్ కంప్లీషన్](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Electra తో పేరు ఎంటిటీ గుర్తింపు](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [GPT-2 తో టెక్స్ట్ జనరేషన్](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [RoBERTa తో సహజ భాషా అనుమితి](https://huggingface.co/roberta-large-mnli?text=The+dog+was+Lost.+Nobody+lost+any+animal)
|
||||
- [GPT-2 తో టెక్స్ట్ జనరేషన్](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [RoBERTa తో సహజ భాషా అనుమితి](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+Lost.+Nobody+lost+any+animal)
|
||||
- [BART తో సారాంశం](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [DistilBERT తో ప్రశ్న సమాధానం](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [T5 తో అనువాదం](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [DistilBERT తో ప్రశ్న సమాధానం](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [T5 తో అనువాదం](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
కంప్యూటర్ దృష్టిలో:
|
||||
- [VIT తో చిత్ర వర్గీకరణ](https://huggingface.co/google/vit-base-patch16-224)
|
||||
@ -198,8 +198,8 @@ limitations under the License.
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -209,8 +209,8 @@ limitations under the License.
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
@ -99,13 +99,13 @@ checkpoint: 检查点
|
||||
你可以直接在模型页面上测试大多数 [model hub](https://huggingface.co/models) 上的模型。 我们也提供了 [私有模型托管、模型版本管理以及推理API](https://huggingface.co/pricing)。
|
||||
|
||||
这里是一些例子：
|
||||
- [用 BERT 做掩码填词](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [用 BERT 做掩码填词](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [用 Electra 做命名实体识别](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [用 GPT-2 做文本生成](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [用 RoBERTa 做自然语言推理](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [用 GPT-2 做文本生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [用 RoBERTa 做自然语言推理](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [用 DistilBERT 做问答](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [用 T5 做翻译](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [用 DistilBERT 做问答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [用 T5 做翻译](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
**[Write With Transformer](https://transformer.huggingface.co)**，由抱抱脸团队打造，是一个文本生成的官方 demo。
|
||||
|
||||
@ -151,8 +151,8 @@ checkpoint: 检查点
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -161,8 +161,8 @@ checkpoint: 检查点
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
@ -111,13 +111,13 @@ user: 使用者
|
||||
你可以直接在 [model hub](https://huggingface.co/models) 上測試大多數的模型。我們也提供了 [私有模型託管、模型版本管理以及推論API](https://huggingface.co/pricing)。
|
||||
|
||||
這裡是一些範例：
|
||||
- [用 BERT 做遮蓋填詞](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [用 BERT 做遮蓋填詞](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [用 Electra 做專有名詞辨識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [用 GPT-2 做文本生成](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [用 RoBERTa 做自然語言推論](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [用 GPT-2 做文本生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
|
||||
- [用 RoBERTa 做自然語言推論](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [用 DistilBERT 做問答](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [用 T5 做翻譯](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
- [用 DistilBERT 做問答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [用 T5 做翻譯](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
**[Write With Transformer](https://transformer.huggingface.co)**，由 Hugging Face 團隊所打造，是一個文本生成的官方 demo。
|
||||
|
||||
@ -163,8 +163,8 @@ user: 使用者
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
@ -173,8 +173,8 @@ user: 使用者
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
@ -42,7 +42,7 @@ Sind Sie unsicher, ob das Modell, das Sie verwenden möchten, bereits eine entsp
|
||||
|
||||
|
||||
Überprüfen Sie das Feld `model_type` in der `config.json` des Modells Ihrer Wahl
|
||||
([Beispiel](https://huggingface.co/bert-base-uncased/blob/main/config.json#L14)). Wenn der entsprechende Modellordner in
|
||||
([Beispiel](https://huggingface.co/google-bert/bert-base-uncased/blob/main/config.json#L14)). Wenn der entsprechende Modellordner in
|
||||
🤗 Transformers eine Datei hat, deren Name mit "modeling_tf" beginnt, bedeutet dies, dass es eine entsprechende TensorFlow
|
||||
Architektur hat ([Beispiel](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert)).
|
||||
|
||||
|
||||
@ -20,7 +20,7 @@ Bei so vielen verschiedenen Transformator-Architekturen kann es eine Herausforde
|
||||
|
||||
<Tip>
|
||||
|
||||
Denken Sie daran, dass sich die Architektur auf das Skelett des Modells bezieht und die Checkpoints die Gewichte für eine bestimmte Architektur sind. Zum Beispiel ist [BERT](https://huggingface.co/bert-base-uncased) eine Architektur, während `bert-base-uncased` ein Checkpoint ist. Modell ist ein allgemeiner Begriff, der entweder Architektur oder Prüfpunkt bedeuten kann.
|
||||
Denken Sie daran, dass sich die Architektur auf das Skelett des Modells bezieht und die Checkpoints die Gewichte für eine bestimmte Architektur sind. Zum Beispiel ist [BERT](https://huggingface.co/google-bert/bert-base-uncased) eine Architektur, während `google-bert/bert-base-uncased` ein Checkpoint ist. Modell ist ein allgemeiner Begriff, der entweder Architektur oder Prüfpunkt bedeuten kann.
|
||||
|
||||
</Tip>
|
||||
|
||||
@ -40,7 +40,7 @@ Laden Sie einen Tokenizer mit [`AutoTokenizer.from_pretrained`]:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
Dann tokenisieren Sie Ihre Eingabe wie unten gezeigt:
|
||||
@ -88,7 +88,7 @@ Mit den `AutoModelFor`-Klassen können Sie schließlich ein vortrainiertes Model
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
|
||||
@ -96,7 +96,7 @@ Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur
|
||||
```py
|
||||
>>> from transformers import AutoModelForTokenClassification
|
||||
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
<Tip warning={true}>
|
||||
@ -115,7 +115,7 @@ Mit den Klassen `TFAutoModelFor` schließlich können Sie ein vortrainiertes Mod
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForSequenceClassification
|
||||
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
|
||||
@ -123,7 +123,7 @@ Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForTokenClassification
|
||||
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Im Allgemeinen empfehlen wir, die Klasse "AutoTokenizer" und die Klasse "TFAutoModelFor" zu verwenden, um vortrainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
|
||||
|
||||
@ -173,14 +173,14 @@ Fügen sie [🤗 Datasets](https://huggingface.co/docs/datasets/) zu Ihrem Offli
|
||||
So würden Sie beispielsweise ein Programm in einem normalen Netzwerk mit einer Firewall für externe Instanzen mit dem folgenden Befehl ausführen:
|
||||
|
||||
```bash
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
```
|
||||
|
||||
Führen Sie das gleiche Programm in einer Offline-Instanz mit aus:
|
||||
|
||||
```bash
|
||||
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
```
|
||||
|
||||
Das Skript sollte nun laufen, ohne sich aufzuhängen oder eine Zeitüberschreitung abzuwarten, da es weiß, dass es nur nach lokalen Dateien suchen soll.
|
||||
|
||||
@ -229,4 +229,4 @@ Um sicherzustellen, dass die Benutzer die Fähigkeiten, Grenzen, möglichen Verz
|
||||
* Manuelles Erstellen und Hochladen einer "README.md"-Datei.
|
||||
* Klicken Sie auf die Schaltfläche **Modellkarte bearbeiten** in Ihrem Modell-Repository.
|
||||
|
||||
Werfen Sie einen Blick auf die DistilBert [model card](https://huggingface.co/distilbert-base-uncased) als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei "README.md" einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation [hier](https://huggingface.co/docs/hub/models-cards).
|
||||
Werfen Sie einen Blick auf die DistilBert [model card](https://huggingface.co/distilbert/distilbert-base-uncased) als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei "README.md" einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation [hier](https://huggingface.co/docs/hub/models-cards).
|
||||
@ -76,8 +76,8 @@ Die [`pipeline`] akzeptiert jedes Modell aus dem [Hub](https://huggingface.co/mo
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
Erstellen Sie eine [`pipeline`] für Ihre Aufgabe, und geben Sie das Modell und den Tokenizer an, die Sie geladen haben:
|
||||
|
||||
@ -45,7 +45,7 @@ Laden Sie einen vortrainierten Tokenizer mit [`AutoTokenizer.from_pretrained`]:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
```
|
||||
|
||||
Dann übergeben Sie Ihren Satz an den Tokenizer:
|
||||
|
||||
@ -89,7 +89,7 @@ Importieren sie die [`pipeline`] und spezifizieren sie die Aufgabe, welche sie l
|
||||
>>> classifier = pipeline("sentiment-analysis")
|
||||
```
|
||||
|
||||
Die Pipeline lädt ein standardmäßiges [vortrainiertes Modell](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) und einen Tokenizer für die Stimmungs-Analyse herunter und speichert sie. Jetzt können Sie den "Klassifikator" auf Ihren Zieltext anwenden:
|
||||
Die Pipeline lädt ein standardmäßiges [vortrainiertes Modell](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) und einen Tokenizer für die Stimmungs-Analyse herunter und speichert sie. Jetzt können Sie den "Klassifikator" auf Ihren Zieltext anwenden:
|
||||
|
||||
```py
|
||||
>>> classifier("We are very happy to show you the 🤗 Transformers library.")
|
||||
@ -407,7 +407,7 @@ Beginnen Sie mit dem Import von [`AutoConfig`] und laden Sie dann das trainierte
|
||||
```py
|
||||
>>> from transformers import AutoConfig
|
||||
|
||||
>>> my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)
|
||||
>>> my_config = AutoConfig.from_pretrained("distilbert/distilbert-base-uncased", n_heads=12)
|
||||
```
|
||||
|
||||
<frameworkcontent>
|
||||
|
||||
@ -87,11 +87,11 @@ pip install -r requirements.txt
|
||||
|
||||
<frameworkcontent>
|
||||
<pt>
|
||||
Das Beispielskript lädt einen Datensatz aus der 🤗 [Datasets](https://huggingface.co/docs/datasets/) Bibliothek herunter und verarbeitet ihn vor. Dann nimmt das Skript eine Feinabstimmung eines Datensatzes mit dem [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) auf einer Architektur vor, die eine Zusammenfassung unterstützt. Das folgende Beispiel zeigt, wie die Feinabstimmung von [T5-small](https://huggingface.co/t5-small) auf dem Datensatz [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) durchgeführt wird. Das T5-Modell benötigt aufgrund der Art und Weise, wie es trainiert wurde, ein zusätzliches Argument `source_prefix`. Mit dieser Eingabeaufforderung weiß T5, dass es sich um eine Zusammenfassungsaufgabe handelt.
|
||||
Das Beispielskript lädt einen Datensatz aus der 🤗 [Datasets](https://huggingface.co/docs/datasets/) Bibliothek herunter und verarbeitet ihn vor. Dann nimmt das Skript eine Feinabstimmung eines Datensatzes mit dem [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) auf einer Architektur vor, die eine Zusammenfassung unterstützt. Das folgende Beispiel zeigt, wie die Feinabstimmung von [T5-small](https://huggingface.co/google-t5/t5-small) auf dem Datensatz [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) durchgeführt wird. Das T5-Modell benötigt aufgrund der Art und Weise, wie es trainiert wurde, ein zusätzliches Argument `source_prefix`. Mit dieser Eingabeaufforderung weiß T5, dass es sich um eine Zusammenfassungsaufgabe handelt.
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -105,11 +105,11 @@ python examples/pytorch/summarization/run_summarization.py \
|
||||
```
|
||||
</pt>
|
||||
<tf>
|
||||
Das Beispielskript lädt einen Datensatz aus der 🤗 [Datasets](https://huggingface.co/docs/datasets/) Bibliothek herunter und verarbeitet ihn vor. Anschließend nimmt das Skript die Feinabstimmung eines Datensatzes mit Keras auf einer Architektur vor, die die Zusammenfassung unterstützt. Das folgende Beispiel zeigt, wie die Feinabstimmung von [T5-small](https://huggingface.co/t5-small) auf dem [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) Datensatz durchgeführt wird. Das T5-Modell benötigt aufgrund der Art und Weise, wie es trainiert wurde, ein zusätzliches Argument `source_prefix`. Mit dieser Eingabeaufforderung weiß T5, dass es sich um eine Zusammenfassungsaufgabe handelt.
|
||||
Das Beispielskript lädt einen Datensatz aus der 🤗 [Datasets](https://huggingface.co/docs/datasets/) Bibliothek herunter und verarbeitet ihn vor. Anschließend nimmt das Skript die Feinabstimmung eines Datensatzes mit Keras auf einer Architektur vor, die die Zusammenfassung unterstützt. Das folgende Beispiel zeigt, wie die Feinabstimmung von [T5-small](https://huggingface.co/google-t5/t5-small) auf dem [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) Datensatz durchgeführt wird. Das T5-Modell benötigt aufgrund der Art und Weise, wie es trainiert wurde, ein zusätzliches Argument `source_prefix`. Mit dieser Eingabeaufforderung weiß T5, dass es sich um eine Zusammenfassungsaufgabe handelt.
|
||||
|
||||
```bash
|
||||
python examples/tensorflow/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--output_dir /tmp/tst-summarization \
|
||||
@ -133,7 +133,7 @@ Der [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) unt
|
||||
torchrun \
|
||||
--nproc_per_node 8 pytorch/summarization/run_summarization.py \
|
||||
--fp16 \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -157,7 +157,7 @@ Tensor Processing Units (TPUs) sind speziell für die Beschleunigung der Leistun
|
||||
```bash
|
||||
python xla_spawn.py --num_cores 8 \
|
||||
summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -176,7 +176,7 @@ Tensor Processing Units (TPUs) sind speziell für die Beschleunigung der Leistun
|
||||
```bash
|
||||
python run_summarization.py \
|
||||
--tpu name_of_tpu_resource \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--output_dir /tmp/tst-summarization \
|
||||
@ -214,7 +214,7 @@ Jetzt sind Sie bereit, das Training zu starten:
|
||||
|
||||
```bash
|
||||
accelerate launch run_summarization_no_trainer.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--source_prefix "summarize: " \
|
||||
@ -233,7 +233,7 @@ Ein Zusammenfassungsskript, das einen benutzerdefinierten Datensatz verwendet, w
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--train_file path_to_csv_or_jsonlines_file \
|
||||
@ -258,7 +258,7 @@ Es ist oft eine gute Idee, Ihr Skript an einer kleineren Anzahl von Beispielen f
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--max_train_samples 50 \
|
||||
--max_eval_samples 50 \
|
||||
--max_predict_samples 50 \
|
||||
@ -288,7 +288,7 @@ Die erste Methode verwendet das Argument `output_dir previous_output_dir`, um da
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -305,7 +305,7 @@ Die zweite Methode verwendet das Argument `Resume_from_checkpoint path_to_specif
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -335,7 +335,7 @@ Das folgende Beispiel zeigt, wie Sie ein Modell mit einem bestimmten Repository-
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
|
||||
@ -48,7 +48,7 @@ Wie Sie nun wissen, benötigen Sie einen Tokenizer, um den Text zu verarbeiten u
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
|
||||
|
||||
>>> def tokenize_function(examples):
|
||||
@ -86,7 +86,7 @@ Beginnen Sie mit dem Laden Ihres Modells und geben Sie die Anzahl der erwarteten
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -187,7 +187,7 @@ Wir können sie also ohne Tokenisierung direkt in ein NumPy-Array konvertieren!
|
||||
```py
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
tokenized_data = tokenizer(dataset["text"], return_tensors="np", padding=True)
|
||||
# Tokenizer returns a BatchEncoding, but we convert that to a dict for Keras
|
||||
tokenized_data = dict(tokenized_data)
|
||||
@ -202,7 +202,7 @@ from transformers import TFAutoModelForSequenceClassification
|
||||
from tensorflow.keras.optimizers import Adam
|
||||
|
||||
# Load and compile our model
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")
|
||||
# Lower learning rates are often better for fine-tuning transformers
|
||||
model.compile(optimizer=Adam(3e-5))
|
||||
|
||||
@ -333,7 +333,7 @@ Laden Sie Ihr Modell mit der Anzahl der erwarteten Kennzeichnungen:
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
|
||||
```
|
||||
|
||||
### Optimierer und Lernratensteuerung
|
||||
|
||||
@ -42,7 +42,7 @@ Are you unsure whether the model you wish to use already has a corresponding Ten
|
||||
|
||||
|
||||
Check the `model_type` field of the `config.json` of your model of choice
|
||||
([example](https://huggingface.co/bert-base-uncased/blob/main/config.json#L14)). If the corresponding model folder in
|
||||
([example](https://huggingface.co/google-bert/bert-base-uncased/blob/main/config.json#L14)). If the corresponding model folder in
|
||||
🤗 Transformers has a file whose name starts with "modeling_tf", it means that it has a corresponding TensorFlow
|
||||
architecture ([example](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert)).
|
||||
|
||||
|
||||
@ -20,7 +20,7 @@ With so many different Transformer architectures, it can be challenging to creat
|
||||
|
||||
<Tip>
|
||||
|
||||
Remember, architecture refers to the skeleton of the model and checkpoints are the weights for a given architecture. For example, [BERT](https://huggingface.co/bert-base-uncased) is an architecture, while `bert-base-uncased` is a checkpoint. Model is a general term that can mean either architecture or checkpoint.
|
||||
Remember, architecture refers to the skeleton of the model and checkpoints are the weights for a given architecture. For example, [BERT](https://huggingface.co/google-bert/bert-base-uncased) is an architecture, while `google-bert/bert-base-uncased` is a checkpoint. Model is a general term that can mean either architecture or checkpoint.
|
||||
|
||||
</Tip>
|
||||
|
||||
@ -42,7 +42,7 @@ Load a tokenizer with [`AutoTokenizer.from_pretrained`]:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
Then tokenize your input as shown below:
|
||||
@ -143,7 +143,7 @@ The `AutoModelFor` classes let you load a pretrained model for a given task (see
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Easily reuse the same checkpoint to load an architecture for a different task:
|
||||
@ -151,7 +151,7 @@ Easily reuse the same checkpoint to load an architecture for a different task:
|
||||
```py
|
||||
>>> from transformers import AutoModelForTokenClassification
|
||||
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
<Tip warning={true}>
|
||||
@ -170,7 +170,7 @@ Finally, the `TFAutoModelFor` classes let you load a pretrained model for a give
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForSequenceClassification
|
||||
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Easily reuse the same checkpoint to load an architecture for a different task:
|
||||
@ -178,7 +178,7 @@ Easily reuse the same checkpoint to load an architecture for a different task:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForTokenClassification
|
||||
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Generally, we recommend using the `AutoTokenizer` class and the `TFAutoModelFor` class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next [tutorial](preprocessing), learn how to use your newly loaded tokenizer, image processor, feature extractor and processor to preprocess a dataset for fine-tuning.
|
||||
|
||||
@ -48,7 +48,7 @@ The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an
|
||||
```py
|
||||
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
|
||||
|
||||
>>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> args = PyTorchBenchmarkArguments(models=["google-bert/bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> benchmark = PyTorchBenchmark(args)
|
||||
```
|
||||
</pt>
|
||||
@ -57,7 +57,7 @@ The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an
|
||||
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
|
||||
|
||||
>>> args = TensorFlowBenchmarkArguments(
|
||||
... models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
|
||||
... models=["google-bert/bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
|
||||
... )
|
||||
>>> benchmark = TensorFlowBenchmark(args)
|
||||
```
|
||||
@ -89,20 +89,20 @@ An instantiated benchmark object can then simply be run by calling `benchmark.ru
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.006
|
||||
bert-base-uncased 8 32 0.006
|
||||
bert-base-uncased 8 128 0.018
|
||||
bert-base-uncased 8 512 0.088
|
||||
google-bert/bert-base-uncased 8 8 0.006
|
||||
google-bert/bert-base-uncased 8 32 0.006
|
||||
google-bert/bert-base-uncased 8 128 0.018
|
||||
google-bert/bert-base-uncased 8 512 0.088
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1227
|
||||
bert-base-uncased 8 32 1281
|
||||
bert-base-uncased 8 128 1307
|
||||
bert-base-uncased 8 512 1539
|
||||
google-bert/bert-base-uncased 8 8 1227
|
||||
google-bert/bert-base-uncased 8 32 1281
|
||||
google-bert/bert-base-uncased 8 128 1307
|
||||
google-bert/bert-base-uncased 8 512 1539
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
@ -146,20 +146,20 @@ An instantiated benchmark object can then simply be run by calling `benchmark.ru
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.005
|
||||
bert-base-uncased 8 32 0.008
|
||||
bert-base-uncased 8 128 0.022
|
||||
bert-base-uncased 8 512 0.105
|
||||
google-bert/bert-base-uncased 8 8 0.005
|
||||
google-bert/bert-base-uncased 8 32 0.008
|
||||
google-bert/bert-base-uncased 8 128 0.022
|
||||
google-bert/bert-base-uncased 8 512 0.105
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1330
|
||||
bert-base-uncased 8 32 1330
|
||||
bert-base-uncased 8 128 1330
|
||||
bert-base-uncased 8 512 1770
|
||||
google-bert/bert-base-uncased 8 8 1330
|
||||
google-bert/bert-base-uncased 8 32 1330
|
||||
google-bert/bert-base-uncased 8 128 1330
|
||||
google-bert/bert-base-uncased 8 512 1770
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
@ -197,7 +197,7 @@ when adding the argument `save_to_csv=True` to [`PyTorchBenchmarkArguments`] and
|
||||
[`TensorFlowBenchmarkArguments`] respectively. In this case, every section is saved in a separate
|
||||
_.csv_ file. The path to each _.csv_ file can optionally be defined via the argument data classes.
|
||||
|
||||
Instead of benchmarking pre-trained models via their model identifier, _e.g._ `bert-base-uncased`, the user can
|
||||
Instead of benchmarking pre-trained models via their model identifier, _e.g._ `google-bert/bert-base-uncased`, the user can
|
||||
alternatively benchmark an arbitrary configuration of any available model class. In this case, a `list` of
|
||||
configurations must be inserted with the benchmark args as follows.
|
||||
|
||||
|
||||
@ -42,7 +42,7 @@ You can control the maximum size before sharding with the `max_shard_size` param
|
||||
```py
|
||||
from transformers import AutoModel
|
||||
|
||||
model = AutoModel.from_pretrained("bert-base-cased")
|
||||
model = AutoModel.from_pretrained("google-bert/bert-base-cased")
|
||||
```
|
||||
|
||||
If you save it using [`~PreTrainedModel.save_pretrained`], you will get a new folder with two files: the config of the model and its weights:
|
||||
|
||||
@ -43,8 +43,8 @@ This page regroups resources around 🤗 Transformers developed by the community
|
||||
|[Fine-tune Roberta for sentiment analysis](https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb) | How to fine-tune a Roberta model for sentiment analysis | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [](https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb)|
|
||||
|[Evaluating Question Generation Models](https://github.com/flexudy-pipe/qugeev) | How accurate are the answers to questions generated by your seq2seq transformer model? | [Pascal Zoleko](https://github.com/zolekode) | [](https://colab.research.google.com/drive/1bpsSqCQU-iw_5nNoRm_crPq6FRuJthq_?usp=sharing)|
|
||||
|[Classify text with DistilBERT and Tensorflow](https://github.com/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb) | How to fine-tune DistilBERT for text classification in TensorFlow | [Peter Bayerle](https://github.com/peterbayerle) | [](https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb)|
|
||||
|[Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | How to warm-start a *EncoderDecoderModel* with a *bert-base-uncased* checkpoint for summarization on CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
|
||||
|[Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | How to warm-start a shared *EncoderDecoderModel* with a *roberta-base* checkpoint for summarization on BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
|
||||
|[Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | How to warm-start a *EncoderDecoderModel* with a *google-bert/bert-base-uncased* checkpoint for summarization on CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
|
||||
|[Leverage RoBERTa for Encoder-Decoder Summarization on BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | How to warm-start a shared *EncoderDecoderModel* with a *FacebookAI/roberta-base* checkpoint for summarization on BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
|
||||
|[Fine-tune TAPAS on Sequential Question Answering (SQA)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) | How to fine-tune *TapasForQuestionAnswering* with a *tapas-base* checkpoint on the Sequential Question Answering (SQA) dataset | [Niels Rogge](https://github.com/nielsrogge) | [](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb)|
|
||||
|[Evaluate TAPAS on Table Fact Checking (TabFact)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb) | How to evaluate a fine-tuned *TapasForSequenceClassification* with a *tapas-base-finetuned-tabfact* checkpoint using a combination of the 🤗 datasets and 🤗 transformers libraries | [Niels Rogge](https://github.com/nielsrogge) | [](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb)|
|
||||
|[Fine-tuning mBART for translation](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb) | How to fine-tune mBART using Seq2SeqTrainer for Hindi to English translation | [Vasudev Gupta](https://github.com/vasudevgupta7) | [](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb)|
|
||||
|
||||
@ -87,7 +87,7 @@ DistilBertConfig {
|
||||
Pretrained model attributes can be modified in the [`~PretrainedConfig.from_pretrained`] function:
|
||||
|
||||
```py
|
||||
>>> my_config = DistilBertConfig.from_pretrained("distilbert-base-uncased", activation="relu", attention_dropout=0.4)
|
||||
>>> my_config = DistilBertConfig.from_pretrained("distilbert/distilbert-base-uncased", activation="relu", attention_dropout=0.4)
|
||||
```
|
||||
|
||||
Once you are satisfied with your model configuration, you can save it with [`~PretrainedConfig.save_pretrained`]. Your configuration file is stored as a JSON file in the specified save directory:
|
||||
@ -128,13 +128,13 @@ This creates a model with random values instead of pretrained weights. You won't
|
||||
Create a pretrained model with [`~PreTrainedModel.from_pretrained`]:
|
||||
|
||||
```py
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:
|
||||
|
||||
```py
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
|
||||
```
|
||||
</pt>
|
||||
<tf>
|
||||
@ -152,13 +152,13 @@ This creates a model with random values instead of pretrained weights. You won't
|
||||
Create a pretrained model with [`~TFPreTrainedModel.from_pretrained`]:
|
||||
|
||||
```py
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:
|
||||
|
||||
```py
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
|
||||
```
|
||||
</tf>
|
||||
</frameworkcontent>
|
||||
@ -174,7 +174,7 @@ For example, [`DistilBertForSequenceClassification`] is a base DistilBERT model
|
||||
```py
|
||||
>>> from transformers import DistilBertForSequenceClassification
|
||||
|
||||
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [`DistilBertForQuestionAnswering`] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.
|
||||
@ -182,7 +182,7 @@ Easily reuse this checkpoint for another task by switching to a different model
|
||||
```py
|
||||
>>> from transformers import DistilBertForQuestionAnswering
|
||||
|
||||
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
</pt>
|
||||
<tf>
|
||||
@ -191,7 +191,7 @@ For example, [`TFDistilBertForSequenceClassification`] is a base DistilBERT mode
|
||||
```py
|
||||
>>> from transformers import TFDistilBertForSequenceClassification
|
||||
|
||||
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [`TFDistilBertForQuestionAnswering`] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.
|
||||
@ -199,7 +199,7 @@ Easily reuse this checkpoint for another task by switching to a different model
|
||||
```py
|
||||
>>> from transformers import TFDistilBertForQuestionAnswering
|
||||
|
||||
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
</tf>
|
||||
</frameworkcontent>
|
||||
@ -232,7 +232,7 @@ It is important to remember the vocabulary from a custom tokenizer will be diffe
|
||||
```py
|
||||
>>> from transformers import DistilBertTokenizer
|
||||
|
||||
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Create a fast tokenizer with the [`DistilBertTokenizerFast`] class:
|
||||
@ -240,7 +240,7 @@ Create a fast tokenizer with the [`DistilBertTokenizerFast`] class:
|
||||
```py
|
||||
>>> from transformers import DistilBertTokenizerFast
|
||||
|
||||
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
|
||||
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
|
||||
@ -586,7 +586,7 @@ model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
|
||||
print(model.id)
|
||||
```
|
||||
|
||||
For the task `text-classification`, this returns `'facebook/bart-large-mnli'`, for `translation` it returns `'t5-base`.
|
||||
For the task `text-classification`, this returns `'facebook/bart-large-mnli'`, for `translation` it returns `'google-t5/t5-base`.
|
||||
|
||||
How do we convert this to a tool that the agent can leverage? All tools depend on the superclass `Tool` that holds the
|
||||
main attributes necessary. We'll create a class that inherits from it:
|
||||
|
||||
@ -266,7 +266,7 @@ from transformers import T5ForConditionalGeneration, T5Config
|
||||
import deepspeed
|
||||
|
||||
with deepspeed.zero.Init():
|
||||
config = T5Config.from_pretrained("t5-small")
|
||||
config = T5Config.from_pretrained("google-t5/t5-small")
|
||||
model = T5ForConditionalGeneration(config)
|
||||
```
|
||||
|
||||
@ -276,7 +276,7 @@ For pretrained models, the DeepSped config file needs to have `is_deepspeed_zero
|
||||
from transformers import AutoModel, Trainer, TrainingArguments
|
||||
|
||||
training_args = TrainingArguments(..., deepspeed=ds_config)
|
||||
model = AutoModel.from_pretrained("t5-small")
|
||||
model = AutoModel.from_pretrained("google-t5/t5-small")
|
||||
trainer = Trainer(model=model, args=training_args, ...)
|
||||
```
|
||||
|
||||
@ -601,7 +601,7 @@ To deploy DeepSpeed on multiple GPUs, add the `--num_gpus` parameter. If you wan
|
||||
```bash
|
||||
deepspeed --num_gpus=2 examples/pytorch/translation/run_translation.py \
|
||||
--deepspeed tests/deepspeed/ds_config_zero3.json \
|
||||
--model_name_or_path t5-small --per_device_train_batch_size 1 \
|
||||
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
|
||||
--output_dir output_dir --overwrite_output_dir --fp16 \
|
||||
--do_train --max_train_samples 500 --num_train_epochs 1 \
|
||||
--dataset_name wmt16 --dataset_config "ro-en" \
|
||||
@ -616,7 +616,7 @@ To deploy DeepSpeed on a single GPU, add the `--num_gpus` parameter. It isn't ne
|
||||
```bash
|
||||
deepspeed --num_gpus=1 examples/pytorch/translation/run_translation.py \
|
||||
--deepspeed tests/deepspeed/ds_config_zero2.json \
|
||||
--model_name_or_path t5-small --per_device_train_batch_size 1 \
|
||||
--model_name_or_path google-t5/t5-small --per_device_train_batch_size 1 \
|
||||
--output_dir output_dir --overwrite_output_dir --fp16 \
|
||||
--do_train --max_train_samples 500 --num_train_epochs 1 \
|
||||
--dataset_name wmt16 --dataset_config "ro-en" \
|
||||
@ -949,7 +949,7 @@ import deepspeed
|
||||
ds_config = {...} # deepspeed config object or path to the file
|
||||
# must run before instantiating the model to detect zero 3
|
||||
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
|
||||
model = AutoModel.from_pretrained("gpt2")
|
||||
model = AutoModel.from_pretrained("openai-community/gpt2")
|
||||
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
|
||||
```
|
||||
|
||||
@ -966,7 +966,7 @@ import deepspeed
|
||||
ds_config = {...} # deepspeed config object or path to the file
|
||||
# must run before instantiating the model to detect zero 3
|
||||
dschf = HfDeepSpeedConfig(ds_config) # keep this object alive
|
||||
config = AutoConfig.from_pretrained("gpt2")
|
||||
config = AutoConfig.from_pretrained("openai-community/gpt2")
|
||||
model = AutoModel.from_config(config)
|
||||
engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
|
||||
```
|
||||
|
||||
@ -54,7 +54,7 @@ When you load a model explicitly, you can inspect the generation configuration t
|
||||
```python
|
||||
>>> from transformers import AutoModelForCausalLM
|
||||
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
|
||||
>>> model.generation_config
|
||||
GenerationConfig {
|
||||
"bos_token_id": 50256,
|
||||
@ -121,8 +121,8 @@ one for summarization with beam search). You must have the right Hub permissions
|
||||
```python
|
||||
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
|
||||
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
|
||||
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
|
||||
|
||||
>>> translation_generation_config = GenerationConfig(
|
||||
... num_beams=4,
|
||||
@ -162,8 +162,8 @@ your screen, one word at a time:
|
||||
```python
|
||||
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
|
||||
|
||||
>>> tok = AutoTokenizer.from_pretrained("gpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
|
||||
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
|
||||
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
|
||||
>>> streamer = TextStreamer(tok)
|
||||
|
||||
@ -187,7 +187,7 @@ Here, we'll show some of the parameters that control the decoding strategies and
|
||||
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
>>> prompt = "I look forward to"
|
||||
>>> checkpoint = "distilgpt2"
|
||||
>>> checkpoint = "distilbert/distilgpt2"
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
@ -208,7 +208,7 @@ The two main parameters that enable and control the behavior of contrastive sear
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
>>> checkpoint = "gpt2-large"
|
||||
>>> checkpoint = "openai-community/gpt2-large"
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
||||
>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
|
||||
|
||||
@ -235,7 +235,7 @@ To enable multinomial sampling set `do_sample=True` and `num_beams=1`.
|
||||
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
|
||||
>>> set_seed(0) # For reproducibility
|
||||
|
||||
>>> checkpoint = "gpt2-large"
|
||||
>>> checkpoint = "openai-community/gpt2-large"
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
||||
>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
|
||||
|
||||
@ -260,7 +260,7 @@ To enable this decoding strategy, specify the `num_beams` (aka number of hypothe
|
||||
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
>>> prompt = "It is astonishing how one can"
|
||||
>>> checkpoint = "gpt2-medium"
|
||||
>>> checkpoint = "openai-community/gpt2-medium"
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
@ -283,7 +283,7 @@ the `num_beams` greater than 1, and set `do_sample=True` to use this decoding st
|
||||
>>> set_seed(0) # For reproducibility
|
||||
|
||||
>>> prompt = "translate English to German: The house is wonderful."
|
||||
>>> checkpoint = "t5-small"
|
||||
>>> checkpoint = "google-t5/t5-small"
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
|
||||
@ -34,7 +34,7 @@ For example, consider these two sequences:
|
||||
```python
|
||||
>>> from transformers import BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
|
||||
>>> sequence_a = "This is a short sequence."
|
||||
>>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
|
||||
@ -159,7 +159,7 @@ The process of selecting and transforming raw data into a set of features that a
|
||||
|
||||
In each residual attention block in transformers the self-attention layer is usually followed by 2 feed forward layers.
|
||||
The intermediate embedding size of the feed forward layers is often bigger than the hidden size of the model (e.g., for
|
||||
`bert-base-uncased`).
|
||||
`google-bert/bert-base-uncased`).
|
||||
|
||||
For an input of size `[batch_size, sequence_length]`, the memory required to store the intermediate feed forward
|
||||
embeddings `[batch_size, sequence_length, config.intermediate_size]` can account for a large fraction of the memory
|
||||
@ -212,7 +212,7 @@ tokenizer, which is a [WordPiece](https://arxiv.org/pdf/1609.08144.pdf) tokenize
|
||||
```python
|
||||
>>> from transformers import BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
|
||||
>>> sequence = "A Titan RTX has 24GB of VRAM"
|
||||
```
|
||||
@ -467,7 +467,7 @@ arguments (and not a list, like before) like this:
|
||||
```python
|
||||
>>> from transformers import BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
>>> sequence_a = "HuggingFace is based in NYC"
|
||||
>>> sequence_b = "Where is HuggingFace based?"
|
||||
|
||||
|
||||
@ -179,7 +179,7 @@ Add [🤗 Datasets](https://huggingface.co/docs/datasets/) to your offline train
|
||||
|
||||
```bash
|
||||
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
```
|
||||
|
||||
This script should run without hanging or waiting to timeout because it won't attempt to download the model from the Hub.
|
||||
|
||||
@ -38,8 +38,8 @@ Here's an example:
|
||||
```python
|
||||
from transformers import GPT2Tokenizer, GPT2LMHeadModel
|
||||
|
||||
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
|
||||
model = GPT2LMHeadModel.from_pretrained("gpt2")
|
||||
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
|
||||
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
|
||||
|
||||
inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
|
||||
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
|
||||
|
||||
@ -26,8 +26,8 @@ Let's see how this looks in an example:
|
||||
from transformers import BertTokenizer, BertForSequenceClassification
|
||||
import torch
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
|
||||
tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
||||
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
|
||||
|
||||
@ -43,7 +43,7 @@ If you want to use a specific model from the [hub](https://huggingface.co) you c
|
||||
the hub already defines it:
|
||||
|
||||
```python
|
||||
>>> pipe = pipeline(model="roberta-large-mnli")
|
||||
>>> pipe = pipeline(model="FacebookAI/roberta-large-mnli")
|
||||
>>> pipe("This restaurant is awesome")
|
||||
[{'label': 'NEUTRAL', 'score': 0.7313136458396912}]
|
||||
```
|
||||
|
||||
@ -25,7 +25,7 @@ Instantiating one of [`AutoConfig`], [`AutoModel`], and
|
||||
|
||||
|
||||
```python
|
||||
model = AutoModel.from_pretrained("bert-base-cased")
|
||||
model = AutoModel.from_pretrained("google-bert/bert-base-cased")
|
||||
```
|
||||
|
||||
will create a model that is an instance of [`BertModel`].
|
||||
|
||||
@ -44,15 +44,15 @@ subsequent fine-tuning:
|
||||
```python
|
||||
>>> # leverage checkpoints for Bert2Bert model...
|
||||
>>> # use BERT's cls token as BOS token and sep token as EOS token
|
||||
>>> encoder = BertGenerationEncoder.from_pretrained("bert-large-uncased", bos_token_id=101, eos_token_id=102)
|
||||
>>> encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-large-uncased", bos_token_id=101, eos_token_id=102)
|
||||
>>> # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
|
||||
>>> decoder = BertGenerationDecoder.from_pretrained(
|
||||
... "bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
|
||||
... "google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
|
||||
... )
|
||||
>>> bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
|
||||
|
||||
>>> # create tokenizer...
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
|
||||
|
||||
>>> input_ids = tokenizer(
|
||||
... "This is a long article to summarize", add_special_tokens=False, return_tensors="pt"
|
||||
|
||||
@ -34,7 +34,7 @@ The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, li
|
||||
distilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, a
|
||||
distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108). DistilBERT is a
|
||||
small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
|
||||
*bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
|
||||
*google-bert/bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
|
||||
understanding benchmark.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
@ -152,8 +152,8 @@ To load and run a model using Flash Attention 2, refer to the snippet below:
|
||||
|
||||
>>> device = "cuda" # the device to load the model onto
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
|
||||
>>> model = AutoModel.from_pretrained("distilbert-base-uncased", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained('distilbert/distilbert-base-uncased')
|
||||
>>> model = AutoModel.from_pretrained("distilbert/distilbert-base-uncased", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
|
||||
|
||||
>>> text = "Replace me by any text you'd like."
|
||||
|
||||
|
||||
@ -55,8 +55,8 @@ To do so, the `EncoderDecoderModel` class provides a [`EncoderDecoderModel.from_
|
||||
```python
|
||||
>>> from transformers import EncoderDecoderModel, BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-uncased", "google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
## Loading an existing `EncoderDecoderModel` checkpoint and perform inference.
|
||||
@ -119,8 +119,8 @@ target sequence).
|
||||
```python
|
||||
>>> from transformers import BertTokenizer, EncoderDecoderModel
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-uncased", "google-bert/bert-base-uncased")
|
||||
|
||||
>>> model.config.decoder_start_token_id = tokenizer.cls_token_id
|
||||
>>> model.config.pad_token_id = tokenizer.pad_token_id
|
||||
|
||||
@ -38,7 +38,7 @@ The main differences compared to GPT2.
|
||||
- Use jit to fuse the attention fp32 casting, masking, softmax, and scaling.
|
||||
- Combine the attention and causal masks into a single one, pre-computed for the whole model instead of every layer.
|
||||
- Merge the key and value caches into one (this changes the format of layer_past/ present, does it risk creating problems?)
|
||||
- Use the memory layout (self.num_heads, 3, self.head_dim) instead of `(3, self.num_heads, self.head_dim)` for the QKV tensor with MHA. (prevents an overhead with the merged key and values, but makes the checkpoints incompatible with the original gpt2 model).
|
||||
- Use the memory layout (self.num_heads, 3, self.head_dim) instead of `(3, self.num_heads, self.head_dim)` for the QKV tensor with MHA. (prevents an overhead with the merged key and values, but makes the checkpoints incompatible with the original openai-community/gpt2 model).
|
||||
|
||||
You can read more about the optimizations in the [original pull request](https://github.com/huggingface/transformers/pull/22575)
|
||||
|
||||
|
||||
@ -39,7 +39,7 @@ This model was contributed by [shangz](https://huggingface.co/shangz).
|
||||
- QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to (i) linear layer
|
||||
inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.
|
||||
- QDQBERT requires the dependency of [Pytorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization). To install `pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com`
|
||||
- QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example *bert-base-uncased*), and
|
||||
- QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example *google-bert/bert-base-uncased*), and
|
||||
perform Quantization Aware Training/Post Training Quantization.
|
||||
- A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for
|
||||
SQUAD task can be found at [transformers/examples/research_projects/quantization-qdqbert/](examples/research_projects/quantization-qdqbert/).
|
||||
|
||||
@ -52,7 +52,7 @@ To do so, the `SpeechEncoderDecoderModel` class provides a [`SpeechEncoderDecode
|
||||
>>> from transformers import SpeechEncoderDecoderModel
|
||||
|
||||
>>> model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
... "facebook/hubert-large-ll60k", "bert-base-uncased"
|
||||
... "facebook/hubert-large-ll60k", "google-bert/bert-base-uncased"
|
||||
... )
|
||||
```
|
||||
|
||||
@ -93,7 +93,7 @@ speech inputs) and `labels` (which are the `input_ids` of the encoded target seq
|
||||
>>> from datasets import load_dataset
|
||||
|
||||
>>> encoder_id = "facebook/wav2vec2-base-960h" # acoustic model encoder
|
||||
>>> decoder_id = "bert-base-uncased" # text decoder
|
||||
>>> decoder_id = "google-bert/bert-base-uncased" # text decoder
|
||||
|
||||
>>> feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(decoder_id)
|
||||
|
||||
@ -64,15 +64,15 @@ for summarization: *summarize: ...*.
|
||||
|
||||
T5 comes in different sizes:
|
||||
|
||||
- [t5-small](https://huggingface.co/t5-small)
|
||||
- [google-t5/t5-small](https://huggingface.co/google-t5/t5-small)
|
||||
|
||||
- [t5-base](https://huggingface.co/t5-base)
|
||||
- [google-t5/t5-base](https://huggingface.co/google-t5/t5-base)
|
||||
|
||||
- [t5-large](https://huggingface.co/t5-large)
|
||||
- [google-t5/t5-large](https://huggingface.co/google-t5/t5-large)
|
||||
|
||||
- [t5-3b](https://huggingface.co/t5-3b)
|
||||
- [google-t5/t5-3b](https://huggingface.co/google-t5/t5-3b)
|
||||
|
||||
- [t5-11b](https://huggingface.co/t5-11b).
|
||||
- [google-t5/t5-11b](https://huggingface.co/google-t5/t5-11b).
|
||||
|
||||
Based on the original T5 model, Google has released some follow-up works:
|
||||
|
||||
@ -121,8 +121,8 @@ processed as follows:
|
||||
```python
|
||||
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
|
||||
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
|
||||
|
||||
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
|
||||
>>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
|
||||
@ -146,8 +146,8 @@ the model as follows:
|
||||
```python
|
||||
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
|
||||
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
|
||||
|
||||
>>> input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
|
||||
>>> labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids
|
||||
@ -183,8 +183,8 @@ ignored. The code example below illustrates all of this.
|
||||
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
|
||||
>>> import torch
|
||||
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
|
||||
|
||||
>>> # the following 2 hyperparameters are task-specific
|
||||
>>> max_source_length = 512
|
||||
@ -258,8 +258,8 @@ generation works in general in encoder-decoder models.
|
||||
```python
|
||||
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
|
||||
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
|
||||
|
||||
>>> input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
|
||||
>>> outputs = model.generate(input_ids)
|
||||
@ -275,8 +275,8 @@ The example above only shows a single example. You can also do batched inference
|
||||
```python
|
||||
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
|
||||
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
|
||||
|
||||
>>> task_prefix = "translate English to German: "
|
||||
>>> # use different length sentences to test batching
|
||||
@ -301,8 +301,8 @@ The predicted tokens will then be placed between the sentinel tokens.
|
||||
```python
|
||||
>>> from transformers import T5Tokenizer, T5ForConditionalGeneration
|
||||
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
|
||||
>>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
|
||||
>>> model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
|
||||
|
||||
>>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
|
||||
|
||||
|
||||
@ -22,7 +22,7 @@ This model is in maintenance mode only, so we won't accept any new PRs changing
|
||||
|
||||
We recommend switching to more recent models for improved security.
|
||||
|
||||
In case you would still like to use `TransfoXL` in your experiments, we recommend using the [Hub checkpoint](https://huggingface.co/transfo-xl-wt103) with a specific revision to ensure you are downloading safe files from the Hub.
|
||||
In case you would still like to use `TransfoXL` in your experiments, we recommend using the [Hub checkpoint](https://huggingface.co/transfo-xl/transfo-xl-wt103) with a specific revision to ensure you are downloading safe files from the Hub.
|
||||
|
||||
You will need to set the environment variable `TRUST_REMOTE_CODE` to `True` in order to allow the
|
||||
usage of `pickle.load()`:
|
||||
@ -33,7 +33,7 @@ from transformers import TransfoXLTokenizer, TransfoXLLMHeadModel
|
||||
|
||||
os.environ["TRUST_REMOTE_CODE"] = "True"
|
||||
|
||||
checkpoint = 'transfo-xl-wt103'
|
||||
checkpoint = 'transfo-xl/transfo-xl-wt103'
|
||||
revision = '40a186da79458c9f9de846edfaea79c412137f97'
|
||||
|
||||
tokenizer = TransfoXLTokenizer.from_pretrained(checkpoint, revision=revision)
|
||||
|
||||
@ -58,7 +58,7 @@ To do so, the `VisionEncoderDecoderModel` class provides a [`VisionEncoderDecode
|
||||
>>> from transformers import VisionEncoderDecoderModel
|
||||
|
||||
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
... "microsoft/swin-base-patch4-window7-224-in22k", "bert-base-uncased"
|
||||
... "microsoft/swin-base-patch4-window7-224-in22k", "google-bert/bert-base-uncased"
|
||||
... )
|
||||
```
|
||||
|
||||
@ -123,9 +123,9 @@ images) and `labels` (which are the `input_ids` of the encoded target sequence).
|
||||
>>> from datasets import load_dataset
|
||||
|
||||
>>> image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
... "google/vit-base-patch16-224-in21k", "bert-base-uncased"
|
||||
... "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased"
|
||||
... )
|
||||
|
||||
>>> model.config.decoder_start_token_id = tokenizer.cls_token_id
|
||||
|
||||
@ -73,7 +73,7 @@ The following example shows how to get the last hidden state using [`VisualBertM
|
||||
>>> from transformers import BertTokenizer, VisualBertModel
|
||||
|
||||
>>> model = VisualBertModel.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("What is the man eating?", return_tensors="pt")
|
||||
>>> # this is a custom function that returns the visual embeddings given the image path
|
||||
|
||||
@ -92,7 +92,7 @@ We see that the kernels alone take up 1.3GB of GPU memory. Now let's see how muc
|
||||
|
||||
## Load Model
|
||||
|
||||
First, we load the `bert-large-uncased` model. We load the model weights directly to the GPU so that we can check
|
||||
First, we load the `google-bert/bert-large-uncased` model. We load the model weights directly to the GPU so that we can check
|
||||
how much space just the weights use.
|
||||
|
||||
|
||||
@ -100,7 +100,7 @@ how much space just the weights use.
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-large-uncased").to("cuda")
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-large-uncased").to("cuda")
|
||||
>>> print_gpu_utilization()
|
||||
GPU memory occupied: 2631 MB.
|
||||
```
|
||||
|
||||
@ -229,4 +229,4 @@ To make sure users understand your model's capabilities, limitations, potential
|
||||
* Manually creating and uploading a `README.md` file.
|
||||
* Clicking on the **Edit model card** button in your model repository.
|
||||
|
||||
Take a look at the DistilBert [model card](https://huggingface.co/distilbert-base-uncased) for a good example of the type of information a model card should include. For more details about other options you can control in the `README.md` file such as a model's carbon footprint or widget examples, refer to the documentation [here](https://huggingface.co/docs/hub/models-cards).
|
||||
Take a look at the DistilBert [model card](https://huggingface.co/distilbert/distilbert-base-uncased) for a good example of the type of information a model card should include. For more details about other options you can control in the `README.md` file such as a model's carbon footprint or widget examples, refer to the documentation [here](https://huggingface.co/docs/hub/models-cards).
|
||||
|
||||
@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
[[open-in-colab]]
|
||||
|
||||
There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not *all* multilingual model usage is different though. Some models, like [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased), can be used just like a monolingual model. This guide will show you how to use multilingual models whose usage differs for inference.
|
||||
There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not *all* multilingual model usage is different though. Some models, like [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased), can be used just like a monolingual model. This guide will show you how to use multilingual models whose usage differs for inference.
|
||||
|
||||
## XLM
|
||||
|
||||
@ -28,24 +28,24 @@ XLM has ten different checkpoints, only one of which is monolingual. The nine re
|
||||
|
||||
The following XLM models use language embeddings to specify the language used at inference:
|
||||
|
||||
- `xlm-mlm-ende-1024` (Masked language modeling, English-German)
|
||||
- `xlm-mlm-enfr-1024` (Masked language modeling, English-French)
|
||||
- `xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
|
||||
- `xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
|
||||
- `xlm-mlm-tlm-xnli15-1024` (Masked language modeling + translation, XNLI languages)
|
||||
- `xlm-clm-enfr-1024` (Causal language modeling, English-French)
|
||||
- `xlm-clm-ende-1024` (Causal language modeling, English-German)
|
||||
- `FacebookAI/xlm-mlm-ende-1024` (Masked language modeling, English-German)
|
||||
- `FacebookAI/xlm-mlm-enfr-1024` (Masked language modeling, English-French)
|
||||
- `FacebookAI/xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
|
||||
- `FacebookAI/xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
|
||||
- `FacebookAI/xlm-mlm-tlm-xnli15-1024` (Masked language modeling + translation, XNLI languages)
|
||||
- `FacebookAI/xlm-clm-enfr-1024` (Causal language modeling, English-French)
|
||||
- `FacebookAI/xlm-clm-ende-1024` (Causal language modeling, English-German)
|
||||
|
||||
Language embeddings are represented as a tensor of the same shape as the `input_ids` passed to the model. The values in these tensors depend on the language used and are identified by the tokenizer's `lang2id` and `id2lang` attributes.
|
||||
|
||||
In this example, load the `xlm-clm-enfr-1024` checkpoint (Causal language modeling, English-French):
|
||||
In this example, load the `FacebookAI/xlm-clm-enfr-1024` checkpoint (Causal language modeling, English-French):
|
||||
|
||||
```py
|
||||
>>> import torch
|
||||
>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
|
||||
|
||||
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
|
||||
>>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
|
||||
>>> tokenizer = XLMTokenizer.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
|
||||
>>> model = XLMWithLMHeadModel.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
|
||||
```
|
||||
|
||||
The `lang2id` attribute of the tokenizer displays this model's languages and their ids:
|
||||
@ -83,8 +83,8 @@ The [run_generation.py](https://github.com/huggingface/transformers/tree/main/ex
|
||||
|
||||
The following XLM models do not require language embeddings during inference:
|
||||
|
||||
- `xlm-mlm-17-1280` (Masked language modeling, 17 languages)
|
||||
- `xlm-mlm-100-1280` (Masked language modeling, 100 languages)
|
||||
- `FacebookAI/xlm-mlm-17-1280` (Masked language modeling, 17 languages)
|
||||
- `FacebookAI/xlm-mlm-100-1280` (Masked language modeling, 100 languages)
|
||||
|
||||
These models are used for generic sentence representations, unlike the previous XLM checkpoints.
|
||||
|
||||
@ -92,8 +92,8 @@ These models are used for generic sentence representations, unlike the previous
|
||||
|
||||
The following BERT models can be used for multilingual tasks:
|
||||
|
||||
- `bert-base-multilingual-uncased` (Masked language modeling + Next sentence prediction, 102 languages)
|
||||
- `bert-base-multilingual-cased` (Masked language modeling + Next sentence prediction, 104 languages)
|
||||
- `google-bert/bert-base-multilingual-uncased` (Masked language modeling + Next sentence prediction, 102 languages)
|
||||
- `google-bert/bert-base-multilingual-cased` (Masked language modeling + Next sentence prediction, 104 languages)
|
||||
|
||||
These models do not require language embeddings during inference. They should identify the language from the
|
||||
context and infer accordingly.
|
||||
@ -102,8 +102,8 @@ context and infer accordingly.
|
||||
|
||||
The following XLM-RoBERTa models can be used for multilingual tasks:
|
||||
|
||||
- `xlm-roberta-base` (Masked language modeling, 100 languages)
|
||||
- `xlm-roberta-large` (Masked language modeling, 100 languages)
|
||||
- `FacebookAI/xlm-roberta-base` (Masked language modeling, 100 languages)
|
||||
- `FacebookAI/xlm-roberta-large` (Masked language modeling, 100 languages)
|
||||
|
||||
XLM-RoBERTa was trained on 2.5TB of newly created and cleaned CommonCrawl data in 100 languages. It provides strong gains over previously released multilingual models like mBERT or XLM on downstream tasks like classification, sequence labeling, and question answering.
|
||||
|
||||
|
||||
@ -116,7 +116,7 @@ Each new generation provides a faster bandwidth, e.g. here is a quote from [Nvid
|
||||
|
||||
So the higher `X` you get in the report of `NVX` in the output of `nvidia-smi topo -m` the better. The generation will depend on your GPU architecture.
|
||||
|
||||
Let's compare the execution of a gpt2 language model training over a small sample of wikitext.
|
||||
Let's compare the execution of a openai-community/gpt2 language model training over a small sample of wikitext.
|
||||
|
||||
The results are:
|
||||
|
||||
@ -135,7 +135,7 @@ Here is the full benchmark code and outputs:
|
||||
# DDP w/ NVLink
|
||||
|
||||
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 torchrun \
|
||||
--nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
|
||||
--nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path openai-community/gpt2 \
|
||||
--dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train \
|
||||
--output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
|
||||
|
||||
@ -144,7 +144,7 @@ rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 torchrun \
|
||||
# DDP w/o NVLink
|
||||
|
||||
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 torchrun \
|
||||
--nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path gpt2 \
|
||||
--nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py --model_name_or_path openai-community/gpt2 \
|
||||
--dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train
|
||||
--output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
|
||||
|
||||
|
||||
@ -348,7 +348,7 @@ ORT is supported by 🤗 Optimum which can be used in 🤗 Transformers. You'll
|
||||
from optimum.onnxruntime import ORTModelForSequenceClassification
|
||||
|
||||
ort_model = ORTModelForSequenceClassification.from_pretrained(
|
||||
"distilbert-base-uncased-finetuned-sst-2-english",
|
||||
"distilbert/distilbert-base-uncased-finetuned-sst-2-english",
|
||||
export=True,
|
||||
provider="CUDAExecutionProvider",
|
||||
)
|
||||
@ -360,7 +360,7 @@ Now you're free to use the model for inference:
|
||||
from optimum.pipelines import pipeline
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
|
||||
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased-finetuned-sst-2-english")
|
||||
|
||||
pipeline = pipeline(task="text-classification", model=ort_model, tokenizer=tokenizer, device="cuda:0")
|
||||
result = pipeline("Both the music and visual were astounding, not to mention the actors performance.")
|
||||
|
||||
@ -52,7 +52,7 @@ Take an example of the use cases on [Transformers question-answering](https://gi
|
||||
|
||||
- Training with IPEX using BF16 auto mixed precision on CPU:
|
||||
<pre> python run_qa.py \
|
||||
--model_name_or_path bert-base-uncased \
|
||||
--model_name_or_path google-bert/bert-base-uncased \
|
||||
--dataset_name squad \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
|
||||
@ -90,7 +90,7 @@ The following command enables training with 2 processes on one Xeon node, with o
|
||||
export MASTER_ADDR=127.0.0.1
|
||||
mpirun -n 2 -genv OMP_NUM_THREADS=23 \
|
||||
python3 run_qa.py \
|
||||
--model_name_or_path bert-large-uncased \
|
||||
--model_name_or_path google-bert/bert-large-uncased \
|
||||
--dataset_name squad \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
@ -119,7 +119,7 @@ Now, run the following command in node0 and **4DDP** will be enabled in node0 an
|
||||
mpirun -f hostfile -n 4 -ppn 2 \
|
||||
-genv OMP_NUM_THREADS=23 \
|
||||
python3 run_qa.py \
|
||||
--model_name_or_path bert-large-uncased \
|
||||
--model_name_or_path google-bert/bert-large-uncased \
|
||||
--dataset_name squad \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
@ -210,7 +210,7 @@ spec:
|
||||
- torchrun
|
||||
- /workspace/transformers/examples/pytorch/question-answering/run_qa.py
|
||||
- --model_name_or_path
|
||||
- "bert-large-uncased"
|
||||
- "google-bert/bert-large-uncased"
|
||||
- --dataset_name
|
||||
- "squad"
|
||||
- --do_train
|
||||
|
||||
@ -143,7 +143,7 @@ Here is the benchmarking code and outputs:
|
||||
```bash
|
||||
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
|
||||
python examples/pytorch/language-modeling/run_clm.py \
|
||||
--model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
|
||||
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
|
||||
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
|
||||
|
||||
{'train_runtime': 110.5948, 'train_samples_per_second': 1.808, 'epoch': 0.69}
|
||||
@ -154,7 +154,7 @@ python examples/pytorch/language-modeling/run_clm.py \
|
||||
```bash
|
||||
rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 \
|
||||
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
|
||||
--model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
|
||||
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
|
||||
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
|
||||
|
||||
{'train_runtime': 101.9003, 'train_samples_per_second': 1.963, 'epoch': 0.69}
|
||||
@ -165,7 +165,7 @@ torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
|
||||
```bash
|
||||
rm -r /tmp/test-clm; NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 \
|
||||
torchrun --nproc_per_node 2 examples/pytorch/language-modeling/run_clm.py \
|
||||
--model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
|
||||
--model_name_or_path openai-community/gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 \
|
||||
--do_train --output_dir /tmp/test-clm --per_device_train_batch_size 4 --max_steps 200
|
||||
|
||||
{'train_runtime': 131.4367, 'train_samples_per_second': 1.522, 'epoch': 0.69}
|
||||
|
||||
@ -248,7 +248,7 @@ Let's take a closer look at two alternatives to AdamW optimizer:
|
||||
1. `adafactor` which is available in [`Trainer`]
|
||||
2. `adamw_bnb_8bit` is also available in Trainer, but a third-party integration is provided below for demonstration.
|
||||
|
||||
For comparison, for a 3B-parameter model, like “t5-3b”:
|
||||
For comparison, for a 3B-parameter model, like “google-t5/t5-3b”:
|
||||
* A standard AdamW optimizer will need 24GB of GPU memory because it uses 8 bytes for each parameter (8*3 => 24GB)
|
||||
* Adafactor optimizer will need more than 12GB. It uses slightly more than 4 bytes for each parameter, so 4*3 and then some extra.
|
||||
* 8bit BNB quantized optimizer will use only (2*3) 6GB if all optimizer states are quantized.
|
||||
|
||||
@ -45,7 +45,7 @@ pip install torch torchvision torchaudio
|
||||
export TASK_NAME=mrpc
|
||||
|
||||
python examples/pytorch/text-classification/run_glue.py \
|
||||
--model_name_or_path bert-base-cased \
|
||||
--model_name_or_path google-bert/bert-base-cased \
|
||||
--task_name $TASK_NAME \
|
||||
- --use_mps_device \
|
||||
--do_train \
|
||||
|
||||
@ -75,7 +75,7 @@ Let's demonstrate this process with GPT-2.
|
||||
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
|
||||
|
||||
device = "cuda"
|
||||
model_id = "gpt2-large"
|
||||
model_id = "openai-community/gpt2-large"
|
||||
model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
|
||||
```
|
||||
|
||||
@ -185,7 +185,7 @@ def data():
|
||||
yield f"My example {i}"
|
||||
|
||||
|
||||
pipe = pipeline(model="gpt2", device=0)
|
||||
pipe = pipeline(model="openai-community/gpt2", device=0)
|
||||
generated_characters = 0
|
||||
for out in pipe(data()):
|
||||
generated_characters += len(out[0]["generated_text"])
|
||||
|
||||
@ -48,7 +48,7 @@ async def homepage(request):
|
||||
|
||||
|
||||
async def server_loop(q):
|
||||
pipe = pipeline(model="bert-base-uncased")
|
||||
pipe = pipeline(model="google-bert/bert-base-uncased")
|
||||
while True:
|
||||
(string, response_q) = await q.get()
|
||||
out = pipe(string)
|
||||
|
||||
@ -54,7 +54,7 @@ Get started by loading a pretrained tokenizer with the [`AutoTokenizer.from_pret
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
```
|
||||
|
||||
Then pass your text to the tokenizer:
|
||||
|
||||
@ -77,7 +77,7 @@ Start by creating an instance of [`pipeline`] and specifying a task you want to
|
||||
>>> classifier = pipeline("sentiment-analysis")
|
||||
```
|
||||
|
||||
The [`pipeline`] downloads and caches a default [pretrained model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) and tokenizer for sentiment analysis. Now you can use the `classifier` on your target text:
|
||||
The [`pipeline`] downloads and caches a default [pretrained model](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) and tokenizer for sentiment analysis. Now you can use the `classifier` on your target text:
|
||||
|
||||
```py
|
||||
>>> classifier("We are very happy to show you the 🤗 Transformers library.")
|
||||
@ -384,7 +384,7 @@ Start by importing [`AutoConfig`], and then load the pretrained model you want t
|
||||
```py
|
||||
>>> from transformers import AutoConfig
|
||||
|
||||
>>> my_config = AutoConfig.from_pretrained("distilbert-base-uncased", n_heads=12)
|
||||
>>> my_config = AutoConfig.from_pretrained("distilbert/distilbert-base-uncased", n_heads=12)
|
||||
```
|
||||
|
||||
<frameworkcontent>
|
||||
@ -421,7 +421,7 @@ Depending on your task, you'll typically pass the following parameters to [`Trai
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
2. [`TrainingArguments`] contains the model hyperparameters you can change like learning rate, batch size, and the number of epochs to train for. The default values are used if you don't specify any training arguments:
|
||||
@ -443,7 +443,7 @@ Depending on your task, you'll typically pass the following parameters to [`Trai
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
4. Load a dataset:
|
||||
@ -515,7 +515,7 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForSequenceClassification
|
||||
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
2. Load a preprocessing class like a tokenizer, image processor, feature extractor, or processor:
|
||||
@ -523,7 +523,7 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
3. Create a function to tokenize the dataset:
|
||||
|
||||
@ -87,11 +87,11 @@ pip install -r requirements.txt
|
||||
|
||||
<frameworkcontent>
|
||||
<pt>
|
||||
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset with the [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
|
||||
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset with the [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/google-t5/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -105,11 +105,11 @@ python examples/pytorch/summarization/run_summarization.py \
|
||||
```
|
||||
</pt>
|
||||
<tf>
|
||||
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
|
||||
The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/google-t5/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
|
||||
|
||||
```bash
|
||||
python examples/tensorflow/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--output_dir /tmp/tst-summarization \
|
||||
@ -133,7 +133,7 @@ The [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) sup
|
||||
torchrun \
|
||||
--nproc_per_node 8 pytorch/summarization/run_summarization.py \
|
||||
--fp16 \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -157,7 +157,7 @@ Tensor Processing Units (TPUs) are specifically designed to accelerate performan
|
||||
```bash
|
||||
python xla_spawn.py --num_cores 8 \
|
||||
summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -176,7 +176,7 @@ Tensor Processing Units (TPUs) are specifically designed to accelerate performan
|
||||
```bash
|
||||
python run_summarization.py \
|
||||
--tpu name_of_tpu_resource \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--output_dir /tmp/tst-summarization \
|
||||
@ -214,7 +214,7 @@ Now you are ready to launch the training:
|
||||
|
||||
```bash
|
||||
accelerate launch run_summarization_no_trainer.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--source_prefix "summarize: " \
|
||||
@ -233,7 +233,7 @@ A summarization script using a custom dataset would look like this:
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--train_file path_to_csv_or_jsonlines_file \
|
||||
@ -258,7 +258,7 @@ It is often a good idea to run your script on a smaller number of dataset exampl
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--max_train_samples 50 \
|
||||
--max_eval_samples 50 \
|
||||
--max_predict_samples 50 \
|
||||
@ -288,7 +288,7 @@ The first method uses the `output_dir previous_output_dir` argument to resume tr
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -305,7 +305,7 @@ The second method uses the `resume_from_checkpoint path_to_specific_checkpoint`
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -335,7 +335,7 @@ The following example shows how to upload a model with a specific repository nam
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
|
||||
@ -70,10 +70,10 @@ or view help in command line:
|
||||
optimum-cli export onnx --help
|
||||
```
|
||||
|
||||
To export a model's checkpoint from the 🤗 Hub, for example, `distilbert-base-uncased-distilled-squad`, run the following command:
|
||||
To export a model's checkpoint from the 🤗 Hub, for example, `distilbert/distilbert-base-uncased-distilled-squad`, run the following command:
|
||||
|
||||
```bash
|
||||
optimum-cli export onnx --model distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
|
||||
optimum-cli export onnx --model distilbert/distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
|
||||
```
|
||||
|
||||
You should see the logs indicating progress and showing where the resulting `model.onnx` is saved, like this:
|
||||
@ -166,7 +166,7 @@ pip install transformers[onnx]
|
||||
Use `transformers.onnx` package as a Python module to export a checkpoint using a ready-made configuration:
|
||||
|
||||
```bash
|
||||
python -m transformers.onnx --model=distilbert-base-uncased onnx/
|
||||
python -m transformers.onnx --model=distilbert/distilbert-base-uncased onnx/
|
||||
```
|
||||
|
||||
This exports an ONNX graph of the checkpoint defined by the `--model` argument. Pass any checkpoint on the 🤗 Hub or one that's stored locally.
|
||||
@ -177,7 +177,7 @@ load and run the model with ONNX Runtime as follows:
|
||||
>>> from transformers import AutoTokenizer
|
||||
>>> from onnxruntime import InferenceSession
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> session = InferenceSession("onnx/model.onnx")
|
||||
>>> # ONNX Runtime expects NumPy arrays as input
|
||||
>>> inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np")
|
||||
|
||||
@ -268,7 +268,7 @@ In the early days, translation models were mostly monolingual, but recently, the
|
||||
>>> from transformers import pipeline
|
||||
|
||||
>>> text = "translate English to French: Hugging Face is a community-based open-source platform for machine learning."
|
||||
>>> translator = pipeline(task="translation", model="t5-small")
|
||||
>>> translator = pipeline(task="translation", model="google-t5/t5-small")
|
||||
>>> translator(text)
|
||||
[{'translation_text': "Hugging Face est une tribune communautaire de l'apprentissage des machines."}]
|
||||
```
|
||||
|
||||
@ -29,7 +29,7 @@ the left. This means the model cannot see future tokens. GPT-2 is an example of
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [DistilGPT2](https://huggingface.co/distilgpt2) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
|
||||
1. Finetune [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -110,7 +110,7 @@ The next step is to load a DistilGPT2 tokenizer to process the `text` subfield:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to
|
||||
@ -236,7 +236,7 @@ You're ready to start training your model now! Load DistilGPT2 with [`AutoModelF
|
||||
```py
|
||||
>>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
At this point, only three steps remain:
|
||||
@ -300,7 +300,7 @@ Then you can load DistilGPT2 with [`TFAutoModelForCausalLM`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForCausalLM
|
||||
|
||||
>>> model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")
|
||||
>>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
|
||||
|
||||
@ -26,7 +26,7 @@ require a good contextual understanding of an entire sequence. BERT is an exampl
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [DistilRoBERTa](https://huggingface.co/distilroberta-base) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
|
||||
1. Finetune [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -105,7 +105,7 @@ For masked language modeling, the next step is to load a DistilRoBERTa tokenizer
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base")
|
||||
```
|
||||
|
||||
You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process#flatten) method:
|
||||
@ -226,7 +226,7 @@ You're ready to start training your model now! Load DistilRoBERTa with [`AutoMod
|
||||
```py
|
||||
>>> from transformers import AutoModelForMaskedLM
|
||||
|
||||
>>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
|
||||
>>> model = AutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")
|
||||
```
|
||||
|
||||
At this point, only three steps remain:
|
||||
@ -291,7 +291,7 @@ Then you can load DistilRoBERTa with [`TFAutoModelForMaskedLM`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForMaskedLM
|
||||
|
||||
>>> model = TFAutoModelForMaskedLM.from_pretrained("distilroberta-base")
|
||||
>>> model = TFAutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")
|
||||
```
|
||||
|
||||
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
|
||||
|
||||
@ -22,7 +22,7 @@ A multiple choice task is similar to question answering, except several candidat
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [BERT](https://huggingface.co/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.
|
||||
1. Finetune [BERT](https://huggingface.co/google-bert/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -90,7 +90,7 @@ The next step is to load a BERT tokenizer to process the sentence starts and the
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
The preprocessing function you want to create needs to:
|
||||
@ -253,7 +253,7 @@ You're ready to start training your model now! Load BERT with [`AutoModelForMult
|
||||
```py
|
||||
>>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
At this point, only three steps remain:
|
||||
@ -317,7 +317,7 @@ Then you can load BERT with [`TFAutoModelForMultipleChoice`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForMultipleChoice
|
||||
|
||||
>>> model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
|
||||
|
||||
@ -76,7 +76,7 @@ Run inference with decoder-only models with the `text-generation` pipeline:
|
||||
|
||||
>>> torch.manual_seed(0) # doctest: +IGNORE_RESULT
|
||||
|
||||
>>> generator = pipeline('text-generation', model = 'gpt2')
|
||||
>>> generator = pipeline('text-generation', model = 'openai-community/gpt2')
|
||||
>>> prompt = "Hello, I'm a language model"
|
||||
|
||||
>>> generator(prompt, max_length = 30)
|
||||
|
||||
@ -27,7 +27,7 @@ Question answering tasks return an answer given a question. If you've ever asked
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [SQuAD](https://huggingface.co/datasets/squad) dataset for extractive question answering.
|
||||
1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [SQuAD](https://huggingface.co/datasets/squad) dataset for extractive question answering.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -100,7 +100,7 @@ The next step is to load a DistilBERT tokenizer to process the `question` and `c
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
There are a few preprocessing steps particular to question answering tasks you should be aware of:
|
||||
@ -206,7 +206,7 @@ You're ready to start training your model now! Load DistilBERT with [`AutoModelF
|
||||
```py
|
||||
>>> from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
At this point, only three steps remain:
|
||||
@ -271,7 +271,7 @@ Then you can load DistilBERT with [`TFAutoModelForQuestionAnswering`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForQuestionAnswering
|
||||
|
||||
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
|
||||
|
||||
@ -24,7 +24,7 @@ Text classification is a common NLP task that assigns a label or class to text.
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.
|
||||
1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -87,7 +87,7 @@ The next step is to load a DistilBERT tokenizer to preprocess the `text` field:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Create a preprocessing function to tokenize `text` and truncate sequences to be no longer than DistilBERT's maximum input length:
|
||||
@ -169,7 +169,7 @@ You're ready to start training your model now! Load DistilBERT with [`AutoModelF
|
||||
>>> from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained(
|
||||
... "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
|
||||
... "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
|
||||
... )
|
||||
```
|
||||
|
||||
@ -243,7 +243,7 @@ Then you can load DistilBERT with [`TFAutoModelForSequenceClassification`] along
|
||||
>>> from transformers import TFAutoModelForSequenceClassification
|
||||
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained(
|
||||
... "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
|
||||
... "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
|
||||
... )
|
||||
```
|
||||
|
||||
|
||||
@ -27,7 +27,7 @@ Summarization creates a shorter version of a document or an article that capture
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [T5](https://huggingface.co/t5-small) on the California state bill subset of the [BillSum](https://huggingface.co/datasets/billsum) dataset for abstractive summarization.
|
||||
1. Finetune [T5](https://huggingface.co/google-t5/t5-small) on the California state bill subset of the [BillSum](https://huggingface.co/datasets/billsum) dataset for abstractive summarization.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -92,7 +92,7 @@ The next step is to load a T5 tokenizer to process `text` and `summary`:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> checkpoint = "t5-small"
|
||||
>>> checkpoint = "google-t5/t5-small"
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
||||
```
|
||||
|
||||
|
||||
@ -24,7 +24,7 @@ Token classification assigns a label to individual tokens in a sentence. One of
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
|
||||
1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -110,7 +110,7 @@ The next step is to load a DistilBERT tokenizer to preprocess the `tokens` field
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
As you saw in the example `tokens` field above, it looks like the input has already been tokenized. But the input actually hasn't been tokenized yet and you'll need to set `is_split_into_words=True` to tokenize the words into subwords. For example:
|
||||
@ -272,7 +272,7 @@ You're ready to start training your model now! Load DistilBERT with [`AutoModelF
|
||||
>>> from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained(
|
||||
... "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
|
||||
... "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
|
||||
... )
|
||||
```
|
||||
|
||||
@ -343,7 +343,7 @@ Then you can load DistilBERT with [`TFAutoModelForTokenClassification`] along wi
|
||||
>>> from transformers import TFAutoModelForTokenClassification
|
||||
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained(
|
||||
... "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
|
||||
... "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
|
||||
... )
|
||||
```
|
||||
|
||||
|
||||
@ -24,7 +24,7 @@ Translation converts a sequence of text from one language to another. It is one
|
||||
|
||||
This guide will show you how to:
|
||||
|
||||
1. Finetune [T5](https://huggingface.co/t5-small) on the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset to translate English text to French.
|
||||
1. Finetune [T5](https://huggingface.co/google-t5/t5-small) on the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset to translate English text to French.
|
||||
2. Use your finetuned model for inference.
|
||||
|
||||
<Tip>
|
||||
@ -88,7 +88,7 @@ The next step is to load a T5 tokenizer to process the English-French language p
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> checkpoint = "t5-small"
|
||||
>>> checkpoint = "google-t5/t5-small"
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
||||
```
|
||||
|
||||
|
||||
@ -85,8 +85,8 @@ from transformers.utils import check_min_version
|
||||
check_min_version("4.21.0")
|
||||
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="</s>")
|
||||
model = TFAutoModelForCausalLM.from_pretrained("gpt2")
|
||||
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
|
||||
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
|
||||
input_string = ["TensorFlow is"]
|
||||
|
||||
# One line to create an XLA generation function
|
||||
@ -114,8 +114,8 @@ To ensure `xla_generate()` always operates with the same input shapes, you can s
|
||||
import tensorflow as tf
|
||||
from transformers import AutoTokenizer, TFAutoModelForCausalLM
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="</s>")
|
||||
model = TFAutoModelForCausalLM.from_pretrained("gpt2")
|
||||
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
|
||||
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
|
||||
input_string = ["TensorFlow is"]
|
||||
|
||||
xla_generate = tf.function(model.generate, jit_compile=True)
|
||||
@ -135,8 +135,8 @@ import time
|
||||
import tensorflow as tf
|
||||
from transformers import AutoTokenizer, TFAutoModelForCausalLM
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("gpt2", padding_side="left", pad_token="</s>")
|
||||
model = TFAutoModelForCausalLM.from_pretrained("gpt2")
|
||||
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2", padding_side="left", pad_token="</s>")
|
||||
model = TFAutoModelForCausalLM.from_pretrained("openai-community/gpt2")
|
||||
|
||||
xla_generate = tf.function(model.generate, jit_compile=True)
|
||||
|
||||
|
||||
@ -38,10 +38,10 @@ or view help in command line:
|
||||
optimum-cli export tflite --help
|
||||
```
|
||||
|
||||
To export a model's checkpoint from the 🤗 Hub, for example, `bert-base-uncased`, run the following command:
|
||||
To export a model's checkpoint from the 🤗 Hub, for example, `google-bert/bert-base-uncased`, run the following command:
|
||||
|
||||
```bash
|
||||
optimum-cli export tflite --model bert-base-uncased --sequence_length 128 bert_tflite/
|
||||
optimum-cli export tflite --model google-bert/bert-base-uncased --sequence_length 128 bert_tflite/
|
||||
```
|
||||
|
||||
You should see the logs indicating progress and showing where the resulting `model.tflite` is saved, like this:
|
||||
|
||||
@ -109,7 +109,7 @@ seen before, by decomposing them into known subwords. For instance, the [`~trans
|
||||
```py
|
||||
>>> from transformers import BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> tokenizer.tokenize("I have a new GPU!")
|
||||
["i", "have", "a", "new", "gp", "##u", "!"]
|
||||
```
|
||||
@ -123,7 +123,7 @@ As another example, [`~transformers.XLNetTokenizer`] tokenizes our previously ex
|
||||
```py
|
||||
>>> from transformers import XLNetTokenizer
|
||||
|
||||
>>> tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
|
||||
>>> tokenizer = XLNetTokenizer.from_pretrained("xlnet/xlnet-base-cased")
|
||||
>>> tokenizer.tokenize("Don't you love 🤗 Transformers? We sure do.")
|
||||
["▁Don", "'", "t", "▁you", "▁love", "▁", "🤗", "▁", "Transform", "ers", "?", "▁We", "▁sure", "▁do", "."]
|
||||
```
|
||||
|
||||
@ -97,7 +97,7 @@ class and then save it to disk under the filename `traced_bert.pt`:
|
||||
from transformers import BertModel, BertTokenizer, BertConfig
|
||||
import torch
|
||||
|
||||
enc = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
enc = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
# Tokenizing input text
|
||||
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
|
||||
@ -132,7 +132,7 @@ model = BertModel(config)
|
||||
model.eval()
|
||||
|
||||
# If you are instantiating the model with *from_pretrained* you can also easily set the TorchScript flag
|
||||
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
|
||||
model = BertModel.from_pretrained("google-bert/bert-base-uncased", torchscript=True)
|
||||
|
||||
# Creating the trace
|
||||
traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
|
||||
|
||||
@ -376,7 +376,7 @@ For example, to run the [run_glue.py](https://github.com/huggingface/transformer
|
||||
```bash
|
||||
accelerate launch \
|
||||
./examples/pytorch/text-classification/run_glue.py \
|
||||
--model_name_or_path bert-base-cased \
|
||||
--model_name_or_path google-bert/bert-base-cased \
|
||||
--task_name $TASK_NAME \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
@ -399,7 +399,7 @@ accelerate launch --num_processes=2 \
|
||||
--fsdp_sharding_strategy=1 \
|
||||
--fsdp_state_dict_type=FULL_STATE_DICT \
|
||||
./examples/pytorch/text-classification/run_glue.py
|
||||
--model_name_or_path bert-base-cased \
|
||||
--model_name_or_path google-bert/bert-base-cased \
|
||||
--task_name $TASK_NAME \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
|
||||
@ -48,7 +48,7 @@ As you now know, you need a tokenizer to process the text and include a padding
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
|
||||
|
||||
>>> def tokenize_function(examples):
|
||||
@ -86,7 +86,7 @@ Start by loading your model and specify the number of expected labels. From the
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -187,7 +187,7 @@ so we can just convert that directly to a NumPy array without tokenization!
|
||||
```py
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
tokenized_data = tokenizer(dataset["sentence"], return_tensors="np", padding=True)
|
||||
# Tokenizer returns a BatchEncoding, but we convert that to a dict for Keras
|
||||
tokenized_data = dict(tokenized_data)
|
||||
@ -202,7 +202,7 @@ from transformers import TFAutoModelForSequenceClassification
|
||||
from tensorflow.keras.optimizers import Adam
|
||||
|
||||
# Load and compile our model
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")
|
||||
# Lower learning rates are often better for fine-tuning transformers
|
||||
model.compile(optimizer=Adam(3e-5)) # No loss argument!
|
||||
|
||||
@ -334,7 +334,7 @@ Load your model with the number of expected labels:
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
|
||||
```
|
||||
|
||||
### Optimizer and learning rate scheduler
|
||||
|
||||
@ -134,7 +134,7 @@ In some cases, the output `hidden_state` may be incorrect if the `input_ids` inc
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
>>> import torch
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")
|
||||
>>> model.config.pad_token_id
|
||||
0
|
||||
```
|
||||
@ -191,8 +191,8 @@ For instance, you'll see this error in the following example because there is no
|
||||
```py
|
||||
>>> from transformers import AutoProcessor, AutoModelForQuestionAnswering
|
||||
|
||||
>>> processor = AutoProcessor.from_pretrained("gpt2-medium")
|
||||
>>> model = AutoModelForQuestionAnswering.from_pretrained("gpt2-medium")
|
||||
>>> processor = AutoProcessor.from_pretrained("openai-community/gpt2-medium")
|
||||
>>> model = AutoModelForQuestionAnswering.from_pretrained("openai-community/gpt2-medium")
|
||||
ValueError: Unrecognized configuration class <class 'transformers.models.gpt2.configuration_gpt2.GPT2Config'> for this kind of AutoModel: AutoModelForQuestionAnswering.
|
||||
Model type should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig, ...
|
||||
```
|
||||
|
||||
@ -20,7 +20,7 @@ Con tantas arquitecturas diferentes de Transformer puede ser retador crear una p
|
||||
|
||||
<Tip>
|
||||
|
||||
Recuerda, la arquitectura se refiere al esqueleto del modelo y los checkpoints son los pesos para una arquitectura dada. Por ejemplo, [BERT](https://huggingface.co/bert-base-uncased) es una arquitectura, mientras que `bert-base-uncased` es un checkpoint. Modelo es un término general que puede significar una arquitectura o un checkpoint.
|
||||
Recuerda, la arquitectura se refiere al esqueleto del modelo y los checkpoints son los pesos para una arquitectura dada. Por ejemplo, [BERT](https://huggingface.co/google-bert/bert-base-uncased) es una arquitectura, mientras que `google-bert/bert-base-uncased` es un checkpoint. Modelo es un término general que puede significar una arquitectura o un checkpoint.
|
||||
|
||||
</Tip>
|
||||
|
||||
@ -40,7 +40,7 @@ Carga un tokenizador con [`AutoTokenizer.from_pretrained`]:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
Luego tokeniza tu input como lo mostrado a continuación:
|
||||
@ -88,7 +88,7 @@ Finalmente, las clases `AutoModelFor` te permiten cargar un modelo preentrenado
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Reutiliza fácilmente el mismo checkpoint para cargar una aquitectura para alguna tarea diferente:
|
||||
@ -96,7 +96,7 @@ Reutiliza fácilmente el mismo checkpoint para cargar una aquitectura para algun
|
||||
```py
|
||||
>>> from transformers import AutoModelForTokenClassification
|
||||
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Generalmente recomendamos utilizar las clases `AutoTokenizer` y `AutoModelFor` para cargar instancias pre-entrenadas de modelos. Ésto asegurará que cargues la arquitectura correcta en cada ocasión. En el siguiente [tutorial](preprocessing), aprende a usar tu tokenizador recién cargado, el extractor de características y el procesador para preprocesar un dataset para fine-tuning.
|
||||
@ -107,7 +107,7 @@ Finalmente, la clase `TFAutoModelFor` te permite cargar tu modelo pre-entrenado
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForSequenceClassification
|
||||
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Reutiliza fácilmente el mismo checkpoint para cargar una aquitectura para alguna tarea diferente:
|
||||
@ -115,7 +115,7 @@ Reutiliza fácilmente el mismo checkpoint para cargar una aquitectura para algun
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForTokenClassification
|
||||
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Generalmente recomendamos utilizar las clases `AutoTokenizer` y `TFAutoModelFor` para cargar instancias de modelos pre-entrenados. Ésto asegurará que cargues la arquitectura correcta cada vez. En el siguiente [tutorial](preprocessing), aprende a usar tu tokenizador recién cargado, el extractor de características y el procesador para preprocesar un dataset para fine-tuning.
|
||||
|
||||
@ -43,8 +43,8 @@ Esta página agrupa los recursos de 🤗 Transformers desarrollados por la comun
|
||||
|[Ajustar a Roberta para el análisis de sentimientos](https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb) | Cómo ajustar un modelo de Roberta para el análisis de sentimientos | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [](https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb)|
|
||||
|[Evaluación de modelos de generación de preguntas](https://github.com/flexudy-pipe/qugeev) | ¿Qué tan precisas son las respuestas a las preguntas generadas por tu modelo de transformador seq2seq? | [Pascal Zoleko](https://github.com/zolekode) | [](https://colab.research.google.com/drive/1bpsSqCQU-iw_5nNoRm_crPq6FRuJthq_?usp=sharing)|
|
||||
|[Clasificar texto con DistilBERT y Tensorflow](https://github.com/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb) | Cómo ajustar DistilBERT para la clasificación de texto en TensorFlow | [Peter Bayerle](https://github.com/peterbayerle) | [](https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb)|
|
||||
|[Aprovechar BERT para el resumen de codificador y decodificador en CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | Cómo iniciar en caliente un *EncoderDecoderModel* con un punto de control *bert-base-uncased* para resumir en CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
|
||||
|[Aprovechar RoBERTa para el resumen de codificador-decodificador en BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | Cómo iniciar en caliente un *EncoderDecoderModel* compartido con un punto de control *roberta-base* para resumir en BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
|
||||
|[Aprovechar BERT para el resumen de codificador y decodificador en CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | Cómo iniciar en caliente un *EncoderDecoderModel* con un punto de control *google-bert/bert-base-uncased* para resumir en CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
|
||||
|[Aprovechar RoBERTa para el resumen de codificador-decodificador en BBC XSum](https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb) | Cómo iniciar en caliente un *EncoderDecoderModel* compartido con un punto de control *FacebookAI/roberta-base* para resumir en BBC/XSum | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb)|
|
||||
|[Ajustar TAPAS en Sequential Question Answering (SQA)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) | Cómo ajustar *TapasForQuestionAnswering* con un punto de control *tapas-base* en el conjunto de datos del Sequential Question Answering (SQA) | [Niels Rogge](https://github.com/nielsrogge) | [](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb)|
|
||||
|[Evaluar TAPAS en Table Fact Checking (TabFact)](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb) | Cómo evaluar un *TapasForSequenceClassification* ajustado con un punto de control *tapas-base-finetuned-tabfact* usando una combinación de 🤗 conjuntos de datos y 🤗 bibliotecas de transformadores | [Niels Rogge](https://github.com/nielsrogge) | [](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb)|
|
||||
|[Ajustar de mBART para traducción](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb) | Cómo ajustar mBART utilizando Seq2SeqTrainer para la traducción del hindi al inglés | [Vasudev Gupta](https://github.com/vasudevgupta7) | [](https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb)|
|
||||
|
||||
@ -87,9 +87,9 @@ transformers-cli convert --model_type gpt \
|
||||
Aquí hay un ejemplo del proceso para convertir un modelo OpenAI GPT-2 pre-entrenado (más información [aquí](https://github.com/openai/gpt-2)):
|
||||
|
||||
```bash
|
||||
export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
|
||||
export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/openai-community/gpt2/pretrained/weights
|
||||
|
||||
transformers-cli convert --model_type gpt2 \
|
||||
transformers-cli convert --model_type openai-community/gpt2 \
|
||||
--tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
|
||||
--pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
|
||||
[--config OPENAI_GPT2_CONFIG] \
|
||||
|
||||
@ -86,7 +86,7 @@ DistilBertConfig {
|
||||
Los atributos de los modelos preentrenados pueden ser modificados con la función [`~PretrainedConfig.from_pretrained`]:
|
||||
|
||||
```py
|
||||
>>> my_config = DistilBertConfig.from_pretrained("distilbert-base-uncased", activation="relu", attention_dropout=0.4)
|
||||
>>> my_config = DistilBertConfig.from_pretrained("distilbert/distilbert-base-uncased", activation="relu", attention_dropout=0.4)
|
||||
```
|
||||
|
||||
Cuando estés satisfecho con la configuración de tu modelo, puedes guardarlo con la función [`~PretrainedConfig.save_pretrained`]. Tu configuración se guardará en un archivo JSON dentro del directorio que le especifiques como parámetro.
|
||||
@ -128,13 +128,13 @@ Esto crea un modelo con valores aleatorios, en lugar de crearlo con los pesos de
|
||||
Puedes crear un modelo preentrenado con [`~PreTrainedModel.from_pretrained`]:
|
||||
|
||||
```py
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Cuando cargues tus pesos del preentrenamiento, el modelo por defecto se carga automáticamente si nos lo proporciona 🤗 Transformers. Sin embargo, siempre puedes reemplazar (todos o algunos de) los atributos del modelo por defecto por los tuyos:
|
||||
|
||||
```py
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
|
||||
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
|
||||
```
|
||||
</pt>
|
||||
<tf>
|
||||
@ -153,13 +153,13 @@ Esto crea un modelo con valores aleatorios, en lugar de crearlo con los pesos de
|
||||
Puedes crear un modelo preentrenado con [`~TFPreTrainedModel.from_pretrained`]:
|
||||
|
||||
```py
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Cuando cargues tus pesos del preentrenamiento, el modelo por defecto se carga automáticamente si este nos lo proporciona 🤗 Transformers. Sin embargo, siempre puedes reemplazar (todos o algunos de) los atributos del modelo por defecto por los tuyos:
|
||||
|
||||
```py
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
|
||||
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
|
||||
```
|
||||
</tf>
|
||||
</frameworkcontent>
|
||||
@ -177,7 +177,7 @@ Por ejemplo, [`DistilBertForSequenceClassification`] es un modelo DistilBERT ba
|
||||
```py
|
||||
>>> from transformers import DistilBertForSequenceClassification
|
||||
|
||||
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Puedes reutilizar este punto de guardado o *checkpoint* para otra tarea fácilmente cambiando a una cabeza de un modelo diferente. Para una tarea de respuesta a preguntas, puedes usar la cabeza del modelo [`DistilBertForQuestionAnswering`]. La cabeza de respuesta a preguntas es similar a la de clasificación de secuencias, excepto porque consta de una capa lineal delante de la salida de los *hidden states*.
|
||||
@ -186,7 +186,7 @@ Puedes reutilizar este punto de guardado o *checkpoint* para otra tarea fácilme
|
||||
```py
|
||||
>>> from transformers import DistilBertForQuestionAnswering
|
||||
|
||||
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
</pt>
|
||||
<tf>
|
||||
@ -196,7 +196,7 @@ Por ejemplo, [`TFDistilBertForSequenceClassification`] es un modelo DistilBERT
|
||||
```py
|
||||
>>> from transformers import TFDistilBertForSequenceClassification
|
||||
|
||||
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Puedes reutilizar este punto de guardado o *checkpoint* para otra tarea fácilmente cambiando a una cabeza de un modelo diferente. Para una tarea de respuesta a preguntas, puedes usar la cabeza del modelo [`TFDistilBertForQuestionAnswering`]. La cabeza de respuesta a preguntas es similar a la de clasificación de secuencias, excepto porque consta de una capa lineal delante de la salida de los *hidden states*.
|
||||
@ -205,7 +205,7 @@ Puedes reutilizar este punto de guardado o *checkpoint* para otra tarea fácilme
|
||||
```py
|
||||
>>> from transformers import TFDistilBertForQuestionAnswering
|
||||
|
||||
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
</tf>
|
||||
</frameworkcontent>
|
||||
@ -239,7 +239,7 @@ Es importante recordar que los vocabularios que provienen de un *tokenizer* pers
|
||||
```py
|
||||
>>> from transformers import DistilBertTokenizer
|
||||
|
||||
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Crea un *tokenizer* rápido con la clase [`DistilBertTokenizerFast`]:
|
||||
@ -248,7 +248,7 @@ Crea un *tokenizer* rápido con la clase [`DistilBertTokenizerFast`]:
|
||||
```py
|
||||
>>> from transformers import DistilBertTokenizerFast
|
||||
|
||||
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
|
||||
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
|
||||
@ -33,7 +33,7 @@ Por ejemplo, considera estas dos secuencias:
|
||||
```python
|
||||
>>> from transformers import BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
|
||||
>>> sequence_a = "This is a short sequence."
|
||||
>>> sequence_b = "This is a rather long sequence. It is at least longer than the sequence A."
|
||||
@ -145,7 +145,7 @@ El proceso de seleccionar y transformar datos crudos en un conjunto de caracter
|
||||
|
||||
### feed forward chunking
|
||||
|
||||
En cada bloque de atención residual en los transformadores, la capa de autoatención suele ir seguida de 2 capas de avance. El tamaño de embedding intermedio de las capas de avance suele ser mayor que el tamaño oculto del modelo (por ejemplo, para `bert-base-uncased`).
|
||||
En cada bloque de atención residual en los transformadores, la capa de autoatención suele ir seguida de 2 capas de avance. El tamaño de embedding intermedio de las capas de avance suele ser mayor que el tamaño oculto del modelo (por ejemplo, para `google-bert/bert-base-uncased`).
|
||||
|
||||
Para una entrada de tamaño `[batch_size, sequence_length]`, la memoria requerida para almacenar los embeddings intermedios de avance `[batch_size, sequence_length, config.intermediate_size]` puede representar una gran fracción del uso de memoria. Los autores de [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) observaron que, dado que el cálculo es independiente de la dimensión `sequence_length`, es matemáticamente equivalente calcular los embeddings de salida de ambas capas de avance `[batch_size, config.hidden_size]_0, ..., [batch_size, config.hidden_size]_n` individualmente y concatenarlos después a `[batch_size, sequence_length, config.hidden_size]` con `n = sequence_length`, lo que intercambia el aumento del tiempo de cálculo por una reducción en el uso de memoria, pero produce un resultado matemáticamente **equivalente**.
|
||||
|
||||
@ -188,7 +188,7 @@ Cada tokenizador funciona de manera diferente, pero el mecanismo subyacente sigu
|
||||
```python
|
||||
>>> from transformers import BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
|
||||
>>> sequence = "A Titan RTX has 24GB of VRAM"
|
||||
```
|
||||
@ -415,7 +415,7 @@ Podemos utilizar nuestro tokenizador para generar automáticamente una oración
|
||||
```python
|
||||
>>> from transformers import BertTokenizer
|
||||
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
>>> sequence_a = "HuggingFace is based in NYC"
|
||||
>>> sequence_b = "Where is HuggingFace based?"
|
||||
|
||||
|
||||
@ -165,14 +165,14 @@ Puedes añadir [🤗 Datasets](https://huggingface.co/docs/datasets/) al flujo d
|
||||
Por ejemplo, normalmente ejecutarías un programa en una red normal con firewall para instancias externas con el siguiente comando:
|
||||
|
||||
```bash
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
```
|
||||
|
||||
Ejecuta este mismo programa en una instancia offline con el siguiente comando:
|
||||
|
||||
```bash
|
||||
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
```
|
||||
|
||||
El script ahora debería ejecutarse sin bloquearse ni esperar a que se agote el tiempo de espera porque sabe que solo debe buscar archivos locales.
|
||||
|
||||
@ -220,4 +220,4 @@ Para asegurarnos que los usuarios entiendan las capacidades de tu modelo, sus li
|
||||
* Elaborando y subiendo manualmente el archivo`README.md`.
|
||||
* Dando click en el botón **Edit model card** dentro del repositorio.
|
||||
|
||||
Toma un momento para ver la [tarjeta de modelo](https://huggingface.co/distilbert-base-uncased) de DistilBert para que tengas un buen ejemplo del tipo de información que debería incluir. Consulta [la documentación](https://huggingface.co/docs/hub/models-cards) para más detalles acerca de otras opciones que puedes controlar dentro del archivo `README.md` como la huella de carbono del modelo o ejemplos de widgets. Consulta la documentación [aquí](https://huggingface.co/docs/hub/models-cards).
|
||||
Toma un momento para ver la [tarjeta de modelo](https://huggingface.co/distilbert/distilbert-base-uncased) de DistilBert para que tengas un buen ejemplo del tipo de información que debería incluir. Consulta [la documentación](https://huggingface.co/docs/hub/models-cards) para más detalles acerca de otras opciones que puedes controlar dentro del archivo `README.md` como la huella de carbono del modelo o ejemplos de widgets. Consulta la documentación [aquí](https://huggingface.co/docs/hub/models-cards).
|
||||
|
||||
@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
[[open-in-colab]]
|
||||
|
||||
Existen varios modelos multilingües en 🤗 Transformers y su uso para inferencia difiere de los modelos monolingües. Sin embargo, no *todos* los usos de los modelos multilingües son diferentes. Algunos modelos, como [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased), pueden utilizarse igual que un modelo monolingüe. Esta guía te enseñará cómo utilizar modelos multilingües cuyo uso difiere en la inferencia.
|
||||
Existen varios modelos multilingües en 🤗 Transformers y su uso para inferencia difiere de los modelos monolingües. Sin embargo, no *todos* los usos de los modelos multilingües son diferentes. Algunos modelos, como [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased), pueden utilizarse igual que un modelo monolingüe. Esta guía te enseñará cómo utilizar modelos multilingües cuyo uso difiere en la inferencia.
|
||||
|
||||
## XLM
|
||||
|
||||
@ -28,24 +28,24 @@ XLM tiene diez checkpoints diferentes de los cuales solo uno es monolingüe. Los
|
||||
|
||||
Los siguientes modelos XLM usan language embeddings para especificar el lenguaje utilizado en la inferencia:
|
||||
|
||||
- `xlm-mlm-ende-1024` (Masked language modeling, English-German)
|
||||
- `xlm-mlm-enfr-1024` (Masked language modeling, English-French)
|
||||
- `xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
|
||||
- `xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
|
||||
- `xlm-mlm-tlm-xnli15-1024` (Masked language modeling + translation, XNLI languages)
|
||||
- `xlm-clm-enfr-1024` (Causal language modeling, English-French)
|
||||
- `xlm-clm-ende-1024` (Causal language modeling, English-German)
|
||||
- `FacebookAI/xlm-mlm-ende-1024` (Masked language modeling, English-German)
|
||||
- `FacebookAI/xlm-mlm-enfr-1024` (Masked language modeling, English-French)
|
||||
- `FacebookAI/xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
|
||||
- `FacebookAI/xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
|
||||
- `FacebookAI/xlm-mlm-tlm-xnli15-1024` (Masked language modeling + translation, XNLI languages)
|
||||
- `FacebookAI/xlm-clm-enfr-1024` (Causal language modeling, English-French)
|
||||
- `FacebookAI/xlm-clm-ende-1024` (Causal language modeling, English-German)
|
||||
|
||||
Los language embeddings son representados como un tensor de la mismas dimensiones que los `input_ids` pasados al modelo. Los valores de estos tensores dependen del idioma utilizado y se identifican mediante los atributos `lang2id` y `id2lang` del tokenizador.
|
||||
|
||||
En este ejemplo, carga el checkpoint `xlm-clm-enfr-1024` (Causal language modeling, English-French):
|
||||
En este ejemplo, carga el checkpoint `FacebookAI/xlm-clm-enfr-1024` (Causal language modeling, English-French):
|
||||
|
||||
```py
|
||||
>>> import torch
|
||||
>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
|
||||
|
||||
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
|
||||
>>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
|
||||
>>> tokenizer = XLMTokenizer.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
|
||||
>>> model = XLMWithLMHeadModel.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
|
||||
```
|
||||
|
||||
El atributo `lang2id` del tokenizador muestra los idiomas de este modelo y sus ids:
|
||||
@ -83,8 +83,8 @@ El script [run_generation.py](https://github.com/huggingface/transformers/tree/m
|
||||
|
||||
Los siguientes modelos XLM no requieren language embeddings durante la inferencia:
|
||||
|
||||
- `xlm-mlm-17-1280` (modelado de lenguaje enmascarado, 17 idiomas)
|
||||
- `xlm-mlm-100-1280` (modelado de lenguaje enmascarado, 100 idiomas)
|
||||
- `FacebookAI/xlm-mlm-17-1280` (modelado de lenguaje enmascarado, 17 idiomas)
|
||||
- `FacebookAI/xlm-mlm-100-1280` (modelado de lenguaje enmascarado, 100 idiomas)
|
||||
|
||||
Estos modelos se utilizan para representaciones genéricas de frases a diferencia de los anteriores checkpoints XLM.
|
||||
|
||||
@ -92,8 +92,8 @@ Estos modelos se utilizan para representaciones genéricas de frases a diferenci
|
||||
|
||||
Los siguientes modelos de BERT pueden utilizarse para tareas multilingües:
|
||||
|
||||
- `bert-base-multilingual-uncased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 102 idiomas)
|
||||
- `bert-base-multilingual-cased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 104 idiomas)
|
||||
- `google-bert/bert-base-multilingual-uncased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 102 idiomas)
|
||||
- `google-bert/bert-base-multilingual-cased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 104 idiomas)
|
||||
|
||||
Estos modelos no requieren language embeddings durante la inferencia. Deben identificar la lengua a partir del
|
||||
contexto e inferir en consecuencia.
|
||||
@ -102,8 +102,8 @@ contexto e inferir en consecuencia.
|
||||
|
||||
Los siguientes modelos de XLM-RoBERTa pueden utilizarse para tareas multilingües:
|
||||
|
||||
- `xlm-roberta-base` (modelado de lenguaje enmascarado, 100 idiomas)
|
||||
- `xlm-roberta-large` (Modelado de lenguaje enmascarado, 100 idiomas)
|
||||
- `FacebookAI/xlm-roberta-base` (modelado de lenguaje enmascarado, 100 idiomas)
|
||||
- `FacebookAI/xlm-roberta-large` (Modelado de lenguaje enmascarado, 100 idiomas)
|
||||
|
||||
XLM-RoBERTa se entrenó con 2,5 TB de datos CommonCrawl recién creados y depurados en 100 idiomas. Proporciona fuertes ventajas sobre los modelos multilingües publicados anteriormente como mBERT o XLM en tareas posteriores como la clasificación, el etiquetado de secuencias y la respuesta a preguntas.
|
||||
|
||||
|
||||
@ -57,7 +57,7 @@ Demostremos este proceso con GPT-2.
|
||||
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
|
||||
|
||||
device = "cuda"
|
||||
model_id = "gpt2-large"
|
||||
model_id = "openai-community/gpt2-large"
|
||||
model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
|
||||
```
|
||||
|
||||
@ -74,8 +74,8 @@ El [`pipeline`] acepta cualquier modelo del [Model Hub](https://huggingface.co/m
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
Crea un [`pipeline`] para tu tarea y específica el modelo y el tokenizador que cargaste:
|
||||
|
||||
@ -45,7 +45,7 @@ Carga un tokenizador pre-entrenado con [`AutoTokenizer.from_pretrained`]:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
```
|
||||
|
||||
A continuación, pasa tu frase al tokenizador:
|
||||
|
||||
@ -87,11 +87,11 @@ pip install -r requirements.txt
|
||||
|
||||
<frameworkcontent>
|
||||
<pt>
|
||||
El script de ejemplo descarga y preprocesa un conjunto de datos de la biblioteca 🤗 [Datasets](https://huggingface.co/docs/datasets/). Luego, el script ajusta un conjunto de datos con [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) en una arquitectura que soporta la tarea de resumen. El siguiente ejemplo muestra cómo ajustar un [T5-small](https://huggingface.co/t5-small) en el conjunto de datos [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail). El modelo T5 requiere un argumento adicional `source_prefix` debido a cómo fue entrenado. Este aviso le permite a T5 saber que se trata de una tarea de resumir.
|
||||
El script de ejemplo descarga y preprocesa un conjunto de datos de la biblioteca 🤗 [Datasets](https://huggingface.co/docs/datasets/). Luego, el script ajusta un conjunto de datos con [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) en una arquitectura que soporta la tarea de resumen. El siguiente ejemplo muestra cómo ajustar un [T5-small](https://huggingface.co/google-t5/t5-small) en el conjunto de datos [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail). El modelo T5 requiere un argumento adicional `source_prefix` debido a cómo fue entrenado. Este aviso le permite a T5 saber que se trata de una tarea de resumir.
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -105,11 +105,11 @@ python examples/pytorch/summarization/run_summarization.py \
|
||||
```
|
||||
</pt>
|
||||
<tf>
|
||||
El script de ejemplo descarga y preprocesa un conjunto de datos de la biblioteca 🤗 [Datasets](https://huggingface.co/docs/datasets/). Luego, el script ajusta un conjunto de datos utilizando Keras en una arquitectura que soporta la tarea de resumir. El siguiente ejemplo muestra cómo ajustar un [T5-small](https://huggingface.co/t5-small) en el conjunto de datos [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail). El modelo T5 requiere un argumento adicional `source_prefix` debido a cómo fue entrenado. Este aviso le permite a T5 saber que se trata de una tarea de resumir.
|
||||
El script de ejemplo descarga y preprocesa un conjunto de datos de la biblioteca 🤗 [Datasets](https://huggingface.co/docs/datasets/). Luego, el script ajusta un conjunto de datos utilizando Keras en una arquitectura que soporta la tarea de resumir. El siguiente ejemplo muestra cómo ajustar un [T5-small](https://huggingface.co/google-t5/t5-small) en el conjunto de datos [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail). El modelo T5 requiere un argumento adicional `source_prefix` debido a cómo fue entrenado. Este aviso le permite a T5 saber que se trata de una tarea de resumir.
|
||||
|
||||
```bash
|
||||
python examples/tensorflow/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--output_dir /tmp/tst-summarization \
|
||||
@ -133,7 +133,7 @@ python examples/tensorflow/summarization/run_summarization.py \
|
||||
torchrun \
|
||||
--nproc_per_node 8 pytorch/summarization/run_summarization.py \
|
||||
--fp16 \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -157,7 +157,7 @@ Las Unidades de Procesamiento de Tensor (TPUs) están diseñadas específicament
|
||||
```bash
|
||||
python xla_spawn.py --num_cores 8 \
|
||||
summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -176,7 +176,7 @@ Las Unidades de Procesamiento de Tensor (TPUs) están diseñadas específicament
|
||||
```bash
|
||||
python run_summarization.py \
|
||||
--tpu name_of_tpu_resource \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--output_dir /tmp/tst-summarization \
|
||||
@ -214,7 +214,7 @@ Todo listo para iniciar el entrenamiento:
|
||||
|
||||
```bash
|
||||
accelerate launch run_summarization_no_trainer.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--dataset_name cnn_dailymail \
|
||||
--dataset_config "3.0.0" \
|
||||
--source_prefix "summarize: " \
|
||||
@ -233,7 +233,7 @@ Un script para resumir que utiliza un conjunto de datos personalizado se vera as
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--train_file path_to_csv_or_jsonlines_file \
|
||||
@ -258,7 +258,7 @@ A veces, es una buena idea ejecutar tu secuencia de comandos en una cantidad men
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py \
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--max_train_samples 50 \
|
||||
--max_eval_samples 50 \
|
||||
--max_predict_samples 50 \
|
||||
@ -288,7 +288,7 @@ El primer método utiliza el argumento `output_dir previous_output_dir` para rea
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -305,7 +305,7 @@ El segundo método utiliza el argumento `resume_from_checkpoint path_to_specific
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
@ -335,7 +335,7 @@ El siguiente ejemplo muestra cómo cargar un modelo con un nombre de repositorio
|
||||
|
||||
```bash
|
||||
python examples/pytorch/summarization/run_summarization.py
|
||||
--model_name_or_path t5-small \
|
||||
--model_name_or_path google-t5/t5-small \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--dataset_name cnn_dailymail \
|
||||
|
||||
@ -137,7 +137,7 @@ optional arguments:
|
||||
Exportar un checkpoint usando una configuración a la medida se puede hacer de la siguiente manera:
|
||||
|
||||
```bash
|
||||
python -m transformers.onnx --model=distilbert-base-uncased onnx/
|
||||
python -m transformers.onnx --model=distilbert/distilbert-base-uncased onnx/
|
||||
```
|
||||
|
||||
que debería mostrar los siguientes registros:
|
||||
@ -152,7 +152,7 @@ All good, model saved at: onnx/model.onnx
|
||||
```
|
||||
|
||||
Esto exporta un grafo ONNX del checkpoint definido por el argumento `--model`.
|
||||
En este ejemplo, es un modelo `distilbert-base-uncased`, pero puede ser cualquier
|
||||
En este ejemplo, es un modelo `distilbert/distilbert-base-uncased`, pero puede ser cualquier
|
||||
checkpoint en Hugging Face Hub o que esté almacenado localmente.
|
||||
|
||||
El archivo `model.onnx` resultante se puede ejecutar en uno de los
|
||||
@ -164,7 +164,7 @@ modelo con [ONNX Runtime](https://onnxruntime.ai/) de la siguiente manera:
|
||||
>>> from transformers import AutoTokenizer
|
||||
>>> from onnxruntime import InferenceSession
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> session = InferenceSession("onnx/model.onnx")
|
||||
>>> # ONNX Runtime expects NumPy arrays as input
|
||||
>>> inputs = tokenizer("Using DistilBERT with ONNX Runtime!", return_tensors="np")
|
||||
@ -201,8 +201,8 @@ y guardar un checkpoint de la siguiente manera:
|
||||
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
||||
|
||||
>>> # Load tokenizer and PyTorch weights form the Hub
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> pt_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> pt_model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> # Save to disk
|
||||
>>> tokenizer.save_pretrained("local-pt-checkpoint")
|
||||
>>> pt_model.save_pretrained("local-pt-checkpoint")
|
||||
@ -220,8 +220,8 @@ python -m transformers.onnx --model=local-pt-checkpoint onnx/
|
||||
>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
|
||||
|
||||
>>> # Load tokenizer and TensorFlow weights from the Hub
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> # Save to disk
|
||||
>>> tokenizer.save_pretrained("local-tf-checkpoint")
|
||||
>>> tf_model.save_pretrained("local-tf-checkpoint")
|
||||
@ -267,7 +267,7 @@ Le puedes pasar una de estas características al argumento `--feature` en el paq
|
||||
Por ejemplo, para exportar un modelo de clasificación de texto, podemos elegir un modelo ya ajustado del Hub y ejecutar:
|
||||
|
||||
```bash
|
||||
python -m transformers.onnx --model=distilbert-base-uncased-finetuned-sst-2-english \
|
||||
python -m transformers.onnx --model=distilbert/distilbert-base-uncased-finetuned-sst-2-english \
|
||||
--feature=sequence-classification onnx/
|
||||
```
|
||||
|
||||
@ -283,7 +283,7 @@ All good, model saved at: onnx/model.onnx
|
||||
```
|
||||
|
||||
Ten en cuenta que, en este caso, los nombres de salida del modelo ajustado son `logits` en lugar de `last_hidden_state`
|
||||
que vimos anteriormente con el checkpoint `distilbert-base-uncased`. Esto es de esperarse ya que el modelo ajustado
|
||||
que vimos anteriormente con el checkpoint `distilbert/distilbert-base-uncased`. Esto es de esperarse ya que el modelo ajustado
|
||||
tiene un cabezal de clasificación secuencial.
|
||||
|
||||
<Tip>
|
||||
@ -362,7 +362,7 @@ instancia proporcionando la configuración del modelo base de la siguiente maner
|
||||
```python
|
||||
>>> from transformers import AutoConfig
|
||||
|
||||
>>> config = AutoConfig.from_pretrained("distilbert-base-uncased")
|
||||
>>> config = AutoConfig.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> onnx_config = DistilBertOnnxConfig(config)
|
||||
```
|
||||
|
||||
@ -393,7 +393,7 @@ exportar DistilBERT con un cabezal de clasificación de secuencias, podríamos u
|
||||
```python
|
||||
>>> from transformers import AutoConfig
|
||||
|
||||
>>> config = AutoConfig.from_pretrained("distilbert-base-uncased")
|
||||
>>> config = AutoConfig.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
>>> onnx_config_for_seq_clf = DistilBertOnnxConfig(config, task="sequence-classification")
|
||||
>>> print(onnx_config_for_seq_clf.outputs)
|
||||
OrderedDict([('logits', {0: 'batch'})])
|
||||
@ -420,7 +420,7 @@ y la ruta para guardar el archivo exportado:
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> onnx_path = Path("model.onnx")
|
||||
>>> model_ckpt = "distilbert-base-uncased"
|
||||
>>> model_ckpt = "distilbert/distilbert-base-uncased"
|
||||
>>> base_model = AutoModel.from_pretrained(model_ckpt)
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
|
||||
|
||||
@ -550,7 +550,7 @@ con la clase `BertConfig` y luego se guarda en el disco con el nombre de archivo
|
||||
from transformers import BertModel, BertTokenizer, BertConfig
|
||||
import torch
|
||||
|
||||
enc = BertTokenizer.from_pretrained("bert-base-uncased")
|
||||
enc = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
|
||||
# Tokenizing input text
|
||||
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
|
||||
@ -585,7 +585,7 @@ model = BertModel(config)
|
||||
model.eval()
|
||||
|
||||
# If you are instantiating the model with *from_pretrained* you can also easily set the TorchScript flag
|
||||
model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
|
||||
model = BertModel.from_pretrained("google-bert/bert-base-uncased", torchscript=True)
|
||||
|
||||
# Creating the trace
|
||||
traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
|
||||
|
||||
@ -26,11 +26,11 @@ El modelado de lenguaje causal predice el siguiente token en una secuencia de to
|
||||
|
||||
El modelado de lenguaje por enmascaramiento predice un token enmascarado en una secuencia, y el modelo puede considerar los tokens bidireccionalmente.
|
||||
|
||||
Esta guía te mostrará cómo realizar fine-tuning [DistilGPT2](https://huggingface.co/distilgpt2) para modelos de lenguaje causales y [DistilRoBERTa](https://huggingface.co/distilroberta-base) para modelos de lenguaje por enmascaramiento en el [r/askscience](https://www.reddit.com/r/askscience/) subdataset [ELI5](https://huggingface.co/datasets/eli5).
|
||||
Esta guía te mostrará cómo realizar fine-tuning [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) para modelos de lenguaje causales y [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) para modelos de lenguaje por enmascaramiento en el [r/askscience](https://www.reddit.com/r/askscience/) subdataset [ELI5](https://huggingface.co/datasets/eli5).
|
||||
|
||||
<Tip>
|
||||
|
||||
Puedes realizar fine-tuning a otras arquitecturas para modelos de lenguaje como [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) y [BERT](https://huggingface.co/bert-base-uncased) siguiendo los mismos pasos presentados en esta guía!
|
||||
Puedes realizar fine-tuning a otras arquitecturas para modelos de lenguaje como [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) y [BERT](https://huggingface.co/google-bert/bert-base-uncased) siguiendo los mismos pasos presentados en esta guía!
|
||||
|
||||
Mira la [página de tarea](https://huggingface.co/tasks/text-generation) para generación de texto y la [página de tarea](https://huggingface.co/tasks/fill-mask) para modelos de lenguajes por enmascaramiento para obtener más información sobre los modelos, datasets, y métricas asociadas.
|
||||
|
||||
@ -81,7 +81,7 @@ Para modelados de lenguaje causales carga el tokenizador DistilGPT2 para procesa
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
<Youtube id="8PmhEIXhBvI"/>
|
||||
@ -91,7 +91,7 @@ Para modelados de lenguaje por enmascaramiento carga el tokenizador DistilRoBERT
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base")
|
||||
```
|
||||
|
||||
Extrae el subcampo `text` desde su estructura anidado con el método [`flatten`](https://huggingface.co/docs/datasets/process#flatten):
|
||||
@ -203,7 +203,7 @@ Para modelados de lenguajes por enmascaramiento usa el mismo [`DataCollatorForLa
|
||||
|
||||
## Modelado de lenguaje causal
|
||||
|
||||
El modelado de lenguaje causal es frecuentemente utilizado para generación de texto. Esta sección te muestra cómo realizar fine-tuning a [DistilGPT2](https://huggingface.co/distilgpt2) para generar nuevo texto.
|
||||
El modelado de lenguaje causal es frecuentemente utilizado para generación de texto. Esta sección te muestra cómo realizar fine-tuning a [DistilGPT2](https://huggingface.co/distilbert/distilgpt2) para generar nuevo texto.
|
||||
|
||||
### Entrenamiento
|
||||
|
||||
@ -214,7 +214,7 @@ Carga DistilGPT2 con [`AutoModelForCausalLM`]:
|
||||
```py
|
||||
>>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
|
||||
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -288,7 +288,7 @@ Carga DistilGPT2 con [`TFAutoModelForCausalLM`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForCausalLM
|
||||
|
||||
>>> model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")
|
||||
>>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
|
||||
```
|
||||
|
||||
Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
|
||||
@ -309,7 +309,7 @@ Llama a [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) par
|
||||
|
||||
## Modelado de lenguaje por enmascaramiento
|
||||
|
||||
El modelado de lenguaje por enmascaramiento es también conocido como una tarea de rellenar la máscara, pues predice un token enmascarado dada una secuencia. Los modelos de lenguaje por enmascaramiento requieren una buena comprensión del contexto de una secuencia entera, en lugar de solo el contexto a la izquierda. Esta sección te enseña como realizar el fine-tuning de [DistilRoBERTa](https://huggingface.co/distilroberta-base) para predecir una palabra enmascarada.
|
||||
El modelado de lenguaje por enmascaramiento es también conocido como una tarea de rellenar la máscara, pues predice un token enmascarado dada una secuencia. Los modelos de lenguaje por enmascaramiento requieren una buena comprensión del contexto de una secuencia entera, en lugar de solo el contexto a la izquierda. Esta sección te enseña como realizar el fine-tuning de [DistilRoBERTa](https://huggingface.co/distilbert/distilroberta-base) para predecir una palabra enmascarada.
|
||||
|
||||
### Entrenamiento
|
||||
|
||||
@ -320,7 +320,7 @@ Carga DistilRoBERTa con [`AutoModelForMaskedlM`]:
|
||||
```py
|
||||
>>> from transformers import AutoModelForMaskedLM
|
||||
|
||||
>>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
|
||||
>>> model = AutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -395,7 +395,7 @@ Carga DistilRoBERTa con [`TFAutoModelForMaskedLM`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForMaskedLM
|
||||
|
||||
>>> model = TFAutoModelForCausalLM.from_pretrained("distilroberta-base")
|
||||
>>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilroberta-base")
|
||||
```
|
||||
|
||||
Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
|
||||
|
||||
@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
|
||||
La tarea de selección múltiple es parecida a la de responder preguntas, con la excepción de que se dan varias opciones de respuesta junto con el contexto. El modelo se entrena para escoger la respuesta correcta
|
||||
entre varias opciones a partir del contexto dado.
|
||||
|
||||
Esta guía te mostrará como hacerle fine-tuning a [BERT](https://huggingface.co/bert-base-uncased) en la configuración `regular` del dataset [SWAG](https://huggingface.co/datasets/swag), de forma
|
||||
Esta guía te mostrará como hacerle fine-tuning a [BERT](https://huggingface.co/google-bert/bert-base-uncased) en la configuración `regular` del dataset [SWAG](https://huggingface.co/datasets/swag), de forma
|
||||
que seleccione la mejor respuesta a partir de varias opciones y algún contexto.
|
||||
|
||||
## Cargar el dataset SWAG
|
||||
@ -58,7 +58,7 @@ Carga el tokenizer de BERT para procesar el comienzo de cada oración y los cuat
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
La función de preprocesmaiento debe hacer lo siguiente:
|
||||
@ -194,7 +194,7 @@ Carga el modelo BERT con [`AutoModelForMultipleChoice`]:
|
||||
```py
|
||||
>>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -274,7 +274,7 @@ Carga el modelo BERT con [`TFAutoModelForMultipleChoice`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForMultipleChoice
|
||||
|
||||
>>> model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
Configura el modelo para entrenarlo con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
|
||||
|
||||
@ -23,7 +23,7 @@ La respuesta a preguntas devuelve una respuesta a partir de una pregunta dada. E
|
||||
- Extractiva: extraer la respuesta a partir del contexto dado.
|
||||
- Abstractiva: generar una respuesta que responda correctamente la pregunta a partir del contexto dado.
|
||||
|
||||
Esta guía te mostrará como hacer fine-tuning de [DistilBERT](https://huggingface.co/distilbert-base-uncased) en el dataset [SQuAD](https://huggingface.co/datasets/squad) para responder preguntas de forma extractiva.
|
||||
Esta guía te mostrará como hacer fine-tuning de [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) en el dataset [SQuAD](https://huggingface.co/datasets/squad) para responder preguntas de forma extractiva.
|
||||
|
||||
<Tip>
|
||||
|
||||
@ -64,7 +64,7 @@ Carga el tokenizer de DistilBERT para procesar los campos `question` (pregunta)
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Hay algunos pasos de preprocesamiento específicos para la tarea de respuesta a preguntas que debes tener en cuenta:
|
||||
@ -164,7 +164,7 @@ Carga el modelo DistilBERT con [`AutoModelForQuestionAnswering`]:
|
||||
```py
|
||||
>>> from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
|
||||
|
||||
>>> model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -247,7 +247,7 @@ Carga el modelo DistilBERT con [`TFAutoModelForQuestionAnswering`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForQuestionAnswering
|
||||
|
||||
>>> model = TFAutoModelForQuestionAnswering("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForQuestionAnswering("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Configura el modelo para entrenarlo con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
|
||||
|
||||
@ -23,7 +23,7 @@ La generación de resúmenes (summarization, en inglés) crea una versión más
|
||||
- Extractiva: Extrae la información más relevante de un documento.
|
||||
- Abstractiva: Genera un texto nuevo que captura la información más importante.
|
||||
|
||||
Esta guía te mostrará cómo puedes hacer fine-tuning del modelo [T5](https://huggingface.co/t5-small) sobre el subset de proyectos de ley del estado de California, dentro del dataset [BillSum](https://huggingface.co/datasets/billsum) para hacer generación de resúmenes abstractiva.
|
||||
Esta guía te mostrará cómo puedes hacer fine-tuning del modelo [T5](https://huggingface.co/google-t5/t5-small) sobre el subset de proyectos de ley del estado de California, dentro del dataset [BillSum](https://huggingface.co/datasets/billsum) para hacer generación de resúmenes abstractiva.
|
||||
|
||||
<Tip>
|
||||
|
||||
@ -65,7 +65,7 @@ Carga el tokenizador T5 para procesar `text` y `summary`:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
|
||||
```
|
||||
|
||||
La función de preprocesamiento necesita:
|
||||
@ -122,7 +122,7 @@ Carga T5 con [`AutoModelForSeq2SeqLM`]:
|
||||
```py
|
||||
>>> from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
|
||||
|
||||
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
|
||||
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -200,7 +200,7 @@ Carga T5 con [`TFAutoModelForSeq2SeqLM`]:
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForSeq2SeqLM
|
||||
|
||||
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")
|
||||
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
|
||||
```
|
||||
|
||||
Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
|
||||
|
||||
@ -48,7 +48,7 @@ Como ya sabes, necesitas un tokenizador para procesar el texto e incluir una est
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
|
||||
|
||||
|
||||
>>> def tokenize_function(examples):
|
||||
@ -78,7 +78,7 @@ Comienza cargando tu modelo y especifica el número de labels previstas. A parti
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
|
||||
```
|
||||
|
||||
<Tip>
|
||||
@ -200,7 +200,7 @@ Carguemos un modelo TensorFlow con el número esperado de labels:
|
||||
>>> import tensorflow as tf
|
||||
>>> from transformers import TFAutoModelForSequenceClassification
|
||||
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
|
||||
```
|
||||
|
||||
A continuación, compila y aplica fine-tuning a tu modelo con [`fit`](https://keras.io/api/models/model_training_apis/) como lo harías con cualquier otro modelo de Keras:
|
||||
@ -275,7 +275,7 @@ Carga tu modelo con el número de labels previstas:
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", num_labels=5)
|
||||
```
|
||||
|
||||
### Optimiza y programa el learning rate
|
||||
|
||||
@ -20,7 +20,7 @@ Avec autant d'architectures Transformer différentes, il peut être difficile d'
|
||||
|
||||
<Tip>
|
||||
|
||||
Rappel, l'architecture fait référence au squelette du modèle et l'ensemble de poids contient les poids pour une architecture donnée. Par exemple, [BERT](https://huggingface.co/bert-base-uncased) est une architecture, tandis que `bert-base-uncased` est un ensemble de poids. Le terme modèle est général et peut signifier soit architecture soit ensemble de poids.
|
||||
Rappel, l'architecture fait référence au squelette du modèle et l'ensemble de poids contient les poids pour une architecture donnée. Par exemple, [BERT](https://huggingface.co/google-bert/bert-base-uncased) est une architecture, tandis que `google-bert/bert-base-uncased` est un ensemble de poids. Le terme modèle est général et peut signifier soit architecture soit ensemble de poids.
|
||||
|
||||
</Tip>
|
||||
|
||||
@ -41,7 +41,7 @@ Chargez un tokenizer avec [`AutoTokenizer.from_pretrained`]:
|
||||
```py
|
||||
>>> from transformers import AutoTokenizer
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
|
||||
```
|
||||
|
||||
Puis, transformez votre texte initial comme montré ci-dessous:
|
||||
@ -99,7 +99,7 @@ Enfin, les classes `AutoModelFor` vous permettent de charger un modèle pré-ent
|
||||
```py
|
||||
>>> from transformers import AutoModelForSequenceClassification
|
||||
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Réutilisez facilement le même ensemble de poids pour charger une architecture pour une tâche différente :
|
||||
@ -107,7 +107,7 @@ Réutilisez facilement le même ensemble de poids pour charger une architecture
|
||||
```py
|
||||
>>> from transformers import AutoModelForTokenClassification
|
||||
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
<Tip warning={true}>
|
||||
@ -126,7 +126,7 @@ Enfin, les classes `TFAutoModelFor` vous permettent de charger un modèle pré-e
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForSequenceClassification
|
||||
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Réutilisez facilement le même ensemble de poids pour charger une architecture pour une tâche différente :
|
||||
@ -134,7 +134,7 @@ Réutilisez facilement le même ensemble de poids pour charger une architecture
|
||||
```py
|
||||
>>> from transformers import TFAutoModelForTokenClassification
|
||||
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
|
||||
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
|
||||
```
|
||||
|
||||
En général, nous recommandons d'utiliser les classes `AutoTokenizer` et `TFAutoModelFor` pour charger des instances pré-entraînées de tokenizers et modèles respectivement. Cela vous permettra de charger la bonne architecture à chaque fois. Dans le prochain [tutoriel](preprocessing), vous apprenez à utiliser un tokenizer, processeur d'image, extracteur de caractéristiques et processeur pour pré-traiter un jeu de données pour le fine-tuning.
|
||||
|
||||
@ -181,7 +181,7 @@ Ajoutez [🤗 Datasets](https://huggingface.co/docs/datasets/) à votre processu
|
||||
|
||||
```bash
|
||||
HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
|
||||
```
|
||||
|
||||
Le script devrait maintenant s'exécuter sans rester en attente ou attendre une expiration, car il n'essaiera pas de télécharger des modèle sur le Hub.
|
||||
|
||||
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Reference in New Issue
Block a user