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124 lines
3.5 KiB
Markdown
124 lines
3.5 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2019-01-22 and contributed to Hugging Face Transformers on 2020-11-16.*
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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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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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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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device_map="auto",
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)
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inputs = tokenizer("Hello, I'm a <mask> model.", return_tensors="pt").to(model.device)
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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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</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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## 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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