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* transformers-cli -> transformers * Chat command works with positional argument * update doc references to transformers-cli * doc headers * deepspeed --------- Co-authored-by: Joao Gante <joao@huggingface.co>
260 lines
9.2 KiB
Markdown
260 lines
9.2 KiB
Markdown
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">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# BERT
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[BERT](https://huggingface.co/papers/1810.04805) is a bidirectional transformer pretrained on unlabeled text to predict masked tokens in a sentence and to predict whether one sentence follows another. The main idea is that by randomly masking some tokens, the model can train on text to the left and right, giving it a more thorough understanding. BERT is also very versatile because its learned language representations can be adapted for other NLP tasks by fine-tuning an additional layer or head.
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You can find all the original BERT checkpoints under the [BERT](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc) collection.
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> [!TIP]
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> Click on the BERT models in the right sidebar for more examples of how to apply BERT to different language tasks.
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The example below demonstrates how to predict the `[MASK]` token with [`Pipeline`], [`AutoModel`], and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="fill-mask",
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model="google-bert/bert-base-uncased",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline("Plants create [MASK] through a process known as photosynthesis.")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"google-bert/bert-base-uncased",
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)
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model = AutoModelForMaskedLM.from_pretrained(
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"google-bert/bert-base-uncased",
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torch_dtype=torch.float16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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inputs = tokenizer("Plants create [MASK] through a process known as photosynthesis.", return_tensors="pt").to("cuda")
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with torch.no_grad():
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outputs = model(**inputs)
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predictions = outputs.logits
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masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
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predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
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predicted_token = tokenizer.decode(predicted_token_id)
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print(f"The predicted token is: {predicted_token}")
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```
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</hfoption>
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<hfoption id="transformers CLI">
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```bash
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echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers run --task fill-mask --model google-bert/bert-base-uncased --device 0
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Inputs should be padded on the right because BERT uses absolute position embeddings.
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## BertConfig
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[[autodoc]] BertConfig
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- all
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## BertTokenizer
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[[autodoc]] BertTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## BertTokenizerFast
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[[autodoc]] BertTokenizerFast
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## BertModel
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[[autodoc]] BertModel
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- forward
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## BertForPreTraining
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[[autodoc]] BertForPreTraining
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- forward
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## BertLMHeadModel
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[[autodoc]] BertLMHeadModel
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- forward
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## BertForMaskedLM
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[[autodoc]] BertForMaskedLM
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- forward
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## BertForNextSentencePrediction
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[[autodoc]] BertForNextSentencePrediction
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- forward
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## BertForSequenceClassification
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[[autodoc]] BertForSequenceClassification
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- forward
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## BertForMultipleChoice
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[[autodoc]] BertForMultipleChoice
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- forward
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## BertForTokenClassification
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[[autodoc]] BertForTokenClassification
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- forward
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## BertForQuestionAnswering
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[[autodoc]] BertForQuestionAnswering
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- forward
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## TFBertTokenizer
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[[autodoc]] TFBertTokenizer
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## TFBertModel
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[[autodoc]] TFBertModel
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- call
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## TFBertForPreTraining
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[[autodoc]] TFBertForPreTraining
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- call
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## TFBertModelLMHeadModel
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[[autodoc]] TFBertLMHeadModel
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- call
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## TFBertForMaskedLM
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[[autodoc]] TFBertForMaskedLM
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- call
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## TFBertForNextSentencePrediction
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[[autodoc]] TFBertForNextSentencePrediction
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- call
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## TFBertForSequenceClassification
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[[autodoc]] TFBertForSequenceClassification
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- call
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## TFBertForMultipleChoice
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[[autodoc]] TFBertForMultipleChoice
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- call
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## TFBertForTokenClassification
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[[autodoc]] TFBertForTokenClassification
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- call
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## TFBertForQuestionAnswering
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[[autodoc]] TFBertForQuestionAnswering
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- call
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## FlaxBertModel
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[[autodoc]] FlaxBertModel
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- __call__
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## FlaxBertForPreTraining
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[[autodoc]] FlaxBertForPreTraining
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- __call__
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## FlaxBertForCausalLM
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[[autodoc]] FlaxBertForCausalLM
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- __call__
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## FlaxBertForMaskedLM
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[[autodoc]] FlaxBertForMaskedLM
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- __call__
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## FlaxBertForNextSentencePrediction
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[[autodoc]] FlaxBertForNextSentencePrediction
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- __call__
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## FlaxBertForSequenceClassification
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[[autodoc]] FlaxBertForSequenceClassification
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- __call__
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## FlaxBertForMultipleChoice
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[[autodoc]] FlaxBertForMultipleChoice
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- __call__
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## FlaxBertForTokenClassification
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[[autodoc]] FlaxBertForTokenClassification
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- __call__
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## FlaxBertForQuestionAnswering
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[[autodoc]] FlaxBertForQuestionAnswering
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- __call__
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## Bert specific outputs
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[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput
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[[autodoc]] models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
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[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput
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