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*This model was released on 2020-10-23 and added to Hugging Face Transformers on 2020-11-27.*

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# 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">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="fill-mask",
    model="moussaKam/barthez",
    dtype=torch.float16,
    device=0
)
pipeline("Les plantes produisent <mask> grâce à un processus appelé photosynthèse.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "moussaKam/barthez",
)
model = AutoModelForMaskedLM.from_pretrained(
    "moussaKam/barthez",
    dtype=torch.float16,
    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
