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transformers/docs/source/en/model_doc/xlm.md

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+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+*This model was released on 2019-01-22 and added to Hugging Face Transformers on 2020-11-16.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# XLM
+
+[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).
+
+You can find all the original XLM checkpoints under the [Facebook AI community](https://huggingface.co/FacebookAI?search_models=xlm-mlm) organization.
+
+> [!TIP]
+> 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.
+
+The example below demonstrates how to predict the `<mask>` token with [`Pipeline`], [`AutoModel`] and from the command line.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(
+    task="fill-mask",
+    model="facebook/xlm-roberta-xl",
+    dtype=torch.float16,
+    device=0
+)
+pipeline("Bonjour, je suis un modèle <mask>.")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "FacebookAI/xlm-mlm-en-2048",
+)
+model = AutoModelForMaskedLM.from_pretrained(
+    "FacebookAI/xlm-mlm-en-2048",
+    dtype=torch.float16,
+    device_map="auto",
+)
+inputs = tokenizer("Hello, I'm a <mask> model.", return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = outputs.logits.argmax(dim=-1)
+
+predicted_token = tokenizer.decode(predictions[0][inputs["input_ids"][0] == tokenizer.mask_token_id])
+print(f"Predicted token: {predicted_token}")
+```
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+echo -e "Plants create <mask> through a process known as photosynthesis." | transformers run --task fill-mask --model FacebookAI/xlm-mlm-en-2048 --device 0
+```
+
+</hfoption>
+</hfoptions>
+
+## XLMConfig
+
+[[autodoc]] XLMConfig
+
+## XLMTokenizer
+
+[[autodoc]] XLMTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+
+## XLM specific outputs
+
+[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput
+
+## XLMModel
+
+[[autodoc]] XLMModel
+    - forward
+
+## XLMWithLMHeadModel
+
+[[autodoc]] XLMWithLMHeadModel
+    - forward
+
+## XLMForSequenceClassification
+
+[[autodoc]] XLMForSequenceClassification
+    - forward
+
+## XLMForMultipleChoice
+
+[[autodoc]] XLMForMultipleChoice
+    - forward
+
+## XLMForTokenClassification
+
+[[autodoc]] XLMForTokenClassification
+    - forward
+
+## XLMForQuestionAnsweringSimple
+
+[[autodoc]] XLMForQuestionAnsweringSimple
+    - forward
+
+## XLMForQuestionAnswering
+
+[[autodoc]] XLMForQuestionAnswering
+    - forward