first commit

docs/source/en/model_doc/barthez.md

@@ -0,0 +1,84 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+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
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was published in HF papers on 2020-10-23 and contributed to Hugging Face Transformers on 2020-11-27.*
+
+
+# BARThez
+
+[BARThez](https://huggingface.co/papers/2010.12321) is a [BART](./bart) model designed for French language tasks. Unlike existing French BERT models, BARThez includes a pretrained encoder-decoder, allowing it to generate text as well. This model is also available as a multilingual variant, mBARThez, by continuing pretraining multilingual BART on a French corpus.
+
+You can find all of the original BARThez checkpoints under the [BARThez](https://huggingface.co/collections/dascim/barthez-670920b569a07aa53e3b6887) collection.
+
+> [!TIP]
+> This model was contributed by [moussakam](https://huggingface.co/moussakam).
+> Refer to the [BART](./bart) docs for more usage examples.
+
+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
+from transformers import pipeline
+
+
+pipeline = pipeline(
+    task="fill-mask",
+    model="moussaKam/barthez",
+    device=0
+)
+pipeline("Les plantes produisent <mask> grâce à un processus appelé photosynthèse.")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import torch
+
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "moussaKam/barthez",
+)
+model = AutoModelForMaskedLM.from_pretrained(
+    "moussaKam/barthez",
+    device_map="auto",
+)
+inputs = tokenizer("Les plantes produisent <mask> grâce à un processus appelé photosynthèse.", return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = outputs.logits
+
+masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
+predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
+predicted_token = tokenizer.decode(predicted_token_id)
+
+print(f"The predicted token is: {predicted_token}")
+```
+
+</hfoption>
+</hfoptions>
+
+## BarthezTokenizer
+
+[[autodoc]] BarthezTokenizer
+
+## BarthezTokenizerFast
+
+[[autodoc]] BarthezTokenizerFast