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* Created model card for XLM model * Revised model card structure and content of XLM model * Update XLM model documentation with improved examples and code snippets for predicting <mask> tokens using Pipeline and AutoModel.
176 lines
4.5 KiB
Markdown
176 lines
4.5 KiB
Markdown
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# XLM
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[XLM](https://huggingface.co/papers/1901.07291) demonstrates cross-lingual pretraining with two approaches, unsupervised training on a single language and supervised training on more than one language with a cross-lingual language model objective. The XLM model supports the causal language modeling objective, masked language modeling, and translation language modeling (an extension of the [BERT](./bert)) masked language modeling objective to multiple language inputs).
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You can find all the original XLM checkpoints under the [Facebook AI community](https://huggingface.co/FacebookAI?search_models=xlm-mlm) organization.
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> [!TIP]
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> Click on the XLM models in the right sidebar for more examples of how to apply XLM to different cross-lingual tasks like classification, translation, and question answering.
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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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```python
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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="facebook/xlm-roberta-xl",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline("Bonjour, je suis un modèle <mask>.")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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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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"FacebookAI/xlm-mlm-en-2048",
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)
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model = AutoModelForMaskedLM.from_pretrained(
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"FacebookAI/xlm-mlm-en-2048",
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torch_dtype=torch.float16,
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device_map="auto",
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)
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inputs = tokenizer("Hello, I'm a <mask> model.", 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.argmax(dim=-1)
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predicted_token = tokenizer.decode(predictions[0][inputs["input_ids"][0] == tokenizer.mask_token_id])
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print(f"Predicted token: {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-cli run --task fill-mask --model FacebookAI/xlm-mlm-en-2048 --device 0
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```
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</hfoption>
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</hfoptions>
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## XLMConfig
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[[autodoc]] XLMConfig
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## XLMTokenizer
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[[autodoc]] XLMTokenizer
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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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## XLM specific outputs
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[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
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<frameworkcontent>
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<pt>
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## XLMModel
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[[autodoc]] XLMModel
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- forward
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## XLMWithLMHeadModel
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[[autodoc]] XLMWithLMHeadModel
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- forward
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## XLMForSequenceClassification
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[[autodoc]] XLMForSequenceClassification
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- forward
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## XLMForMultipleChoice
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[[autodoc]] XLMForMultipleChoice
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- forward
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## XLMForTokenClassification
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[[autodoc]] XLMForTokenClassification
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- forward
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## XLMForQuestionAnsweringSimple
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[[autodoc]] XLMForQuestionAnsweringSimple
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- forward
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## XLMForQuestionAnswering
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[[autodoc]] XLMForQuestionAnswering
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- forward
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</pt>
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<tf>
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## TFXLMModel
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[[autodoc]] TFXLMModel
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- call
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## TFXLMWithLMHeadModel
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[[autodoc]] TFXLMWithLMHeadModel
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- call
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## TFXLMForSequenceClassification
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[[autodoc]] TFXLMForSequenceClassification
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- call
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## TFXLMForMultipleChoice
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[[autodoc]] TFXLMForMultipleChoice
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- call
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## TFXLMForTokenClassification
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[[autodoc]] TFXLMForTokenClassification
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- call
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## TFXLMForQuestionAnsweringSimple
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[[autodoc]] TFXLMForQuestionAnsweringSimple
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- call
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</tf>
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</frameworkcontent>
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