Updated CamemBERT model card to new standardized format (#39227)
* Updated CamemBERT model card to new standardized format * Applied review suggestions for CamemBERT: restored API refs, added examples, badges, and attribution * Updated CamemBERT usage examples, quantization, badges, and format * Updated CamemBERT badges * Fixed CLI Section
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# CamemBERT
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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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<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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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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<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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## Overview
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# CamemBERT
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The CamemBERT model was proposed in [CamemBERT: a Tasty French Language Model](https://huggingface.co/papers/1911.03894) by
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[Louis Martin](https://huggingface.co/louismartin), [Benjamin Muller](https://huggingface.co/benjamin-mlr), [Pedro Javier Ortiz Suárez](https://huggingface.co/pjox), Yoann Dupont, Laurent Romary, Éric Villemonte de la
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Clergerie, [Djamé Seddah](https://huggingface.co/Djame), and [Benoît Sagot](https://huggingface.co/sagot). It is based on Facebook's RoBERTa model released in 2019. It is a model
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trained on 138GB of French text.
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[CamemBERT](https://huggingface.co/papers/1911.03894) is a language model based on [RoBERTa](./roberta), but trained specifically on French text from the OSCAR dataset, making it more effective for French language tasks.
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The abstract from the paper is the following:
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What sets CamemBERT apart is that it learned from a huge, high quality collection of French data, as opposed to mixing lots of languages. This helps it really understand French better than many multilingual models.
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*Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available
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models have either been trained on English data or on the concatenation of data in multiple languages. This makes
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practical use of such models --in all languages except English-- very limited. Aiming to address this issue for French,
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we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the
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performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging,
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dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art
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for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and
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downstream applications for French NLP.*
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Common applications of CamemBERT include masked language modeling (Fill-mask prediction), text classification (sentiment analysis), token classification (entity recognition) and sentence pair classification (entailment tasks).
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This model was contributed by [the ALMAnaCH team (Inria)](https://huggingface.co/almanach). The original code can be found [here](https://camembert-model.fr/).
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You can find all the original CamemBERT checkpoints under the [ALMAnaCH](https://huggingface.co/almanach/models?search=camembert) organization.
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<Tip>
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> [!TIP]
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> This model was contributed by the [ALMAnaCH (Inria)](https://huggingface.co/almanach) team.
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>
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> Click on the CamemBERT models in the right sidebar for more examples of how to apply CamemBERT to different NLP tasks.
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This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples as well
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as the information relative to the inputs and outputs.
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The examples below demonstrate how to predict the `<mask>` token with [`Pipeline`], [`AutoModel`], and from the command line.
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</Tip>
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<hfoptions id="usage">
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## Resources
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<hfoption id="Pipeline">
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- [Text classification task guide](../tasks/sequence_classification)
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- [Token classification task guide](../tasks/token_classification)
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- [Question answering task guide](../tasks/question_answering)
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- [Causal language modeling task guide](../tasks/language_modeling)
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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- [Multiple choice task guide](../tasks/multiple_choice)
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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("fill-mask", model="camembert-base", torch_dtype=torch.float16, device=0)
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pipeline("Le camembert est un délicieux fromage <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 AutoTokenizer, AutoModelForMaskedLM
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tokenizer = AutoTokenizer.from_pretrained("camembert-base")
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model = AutoModelForMaskedLM.from_pretrained("camembert-base", torch_dtype="auto", device_map="auto", attn_implementation="sdpa")
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inputs = tokenizer("Le camembert est un délicieux fromage <mask>.", 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
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masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
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predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
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predicted_token = tokenizer.decode(predicted_token_id)
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print(f"The predicted token is: {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 "Le camembert est un délicieux fromage <mask>." | transformers run --task fill-mask --model camembert-base --device 0
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```
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</hfoption>
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</hfoptions>
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Quantization reduces the memory burden of large models by representing weights in lower precision. Refer to the [Quantization](../quantization/overview) overview for available options.
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The example below uses [bitsandbytes](../quantization/bitsandbytes) quantization to quantize the weights to 8-bits.
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```python
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from transformers import AutoTokenizer, AutoModelForMaskedLM, BitsAndBytesConfig
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import torch
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quant_config = BitsAndBytesConfig(load_in_8bit=True)
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model = AutoModelForMaskedLM.from_pretrained(
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"almanach/camembert-large",
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quantization_config=quant_config,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("almanach/camembert-large")
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inputs = tokenizer("Le camembert est un délicieux fromage <mask>.", 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
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masked_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
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predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
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predicted_token = tokenizer.decode(predicted_token_id)
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print(f"The predicted token is: {predicted_token}")
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```
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## CamembertConfig
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@@ -138,4 +194,3 @@ as the information relative to the inputs and outputs.
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</tf>
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</frameworkcontent>
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