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--- |
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language: fr |
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license: mit |
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datasets: |
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- oscar |
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--- |
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# CamemBERT: a Tasty French Language Model |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [Citation Information](#citation-information) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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## Model Details |
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- **Model Description:** |
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CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. |
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It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. |
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- **Developed by:** Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. |
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- **Model Type:** Fill-Mask |
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- **Language(s):** French |
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- **License:** MIT |
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- **Parent Model:** See the [RoBERTa base model](https://huggingface.co/roberta-base) for more information about the RoBERTa base model. |
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- **Resources for more information:** |
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- [Research Paper](https://arxiv.org/abs/1911.03894) |
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- [Camembert Website](https://camembert-model.fr/) |
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## Uses |
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#### Direct Use |
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This model can be used for Fill-Mask tasks. |
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## Risks, Limitations and Biases |
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**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). |
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This model was pretrinaed on a subcorpus of OSCAR multilingual corpus. Some of the limitations and risks associated with the OSCAR dataset, which are further detailed in the [OSCAR dataset card](https://huggingface.co/datasets/oscar), include the following: |
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> The quality of some OSCAR sub-corpora might be lower than expected, specifically for the lowest-resource languages. |
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> Constructed from Common Crawl, Personal and sensitive information might be present. |
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## Training |
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#### Training Data |
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OSCAR or Open Super-large Crawled Aggregated coRpus is a multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture. |
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#### Training Procedure |
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| Model | #params | Arch. | Training data | |
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|--------------------------------|--------------------------------|-------|-----------------------------------| |
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| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | |
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| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | |
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| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | |
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| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | |
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| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | |
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| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | |
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## Evaluation |
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The model developers evaluated CamemBERT using four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI). |
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## Citation Information |
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```bibtex |
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@inproceedings{martin2020camembert, |
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title={CamemBERT: a Tasty French Language Model}, |
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author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, |
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booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, |
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year={2020} |
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} |
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``` |
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## How to Get Started With the Model |
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##### Load CamemBERT and its sub-word tokenizer : |
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```python |
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from transformers import CamembertModel, CamembertTokenizer |
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# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large". |
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tokenizer = CamembertTokenizer.from_pretrained("camembert-base") |
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camembert = CamembertModel.from_pretrained("camembert-base") |
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camembert.eval() # disable dropout (or leave in train mode to finetune) |
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``` |
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##### Filling masks using pipeline |
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```python |
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from transformers import pipeline |
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camembert_fill_mask = pipeline("fill-mask", model="camembert-base", tokenizer="camembert-base") |
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results = camembert_fill_mask("Le camembert est <mask> :)") |
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# results |
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#[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200}, |
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# {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.10556930303573608, 'token': 2183}, |
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# {'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.03453315049409866, 'token': 26202}, |
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# {'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.03303130343556404, 'token': 528}, |
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# {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.030076518654823303, 'token': 1654}] |
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``` |
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##### Extract contextual embedding features from Camembert output |
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```python |
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import torch |
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# Tokenize in sub-words with SentencePiece |
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tokenized_sentence = tokenizer.tokenize("J'aime le camembert !") |
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# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] |
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# 1-hot encode and add special starting and end tokens |
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encoded_sentence = tokenizer.encode(tokenized_sentence) |
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# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] |
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# NB: Can be done in one step : tokenize.encode("J'aime le camembert !") |
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# Feed tokens to Camembert as a torch tensor (batch dim 1) |
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encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0) |
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embeddings, _ = camembert(encoded_sentence) |
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# embeddings.detach() |
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# embeddings.size torch.Size([1, 10, 768]) |
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# tensor([[[-0.0254, 0.0235, 0.1027, ..., -0.1459, -0.0205, -0.0116], |
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# [ 0.0606, -0.1811, -0.0418, ..., -0.1815, 0.0880, -0.0766], |
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# [-0.1561, -0.1127, 0.2687, ..., -0.0648, 0.0249, 0.0446], |
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# ..., |
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``` |
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##### Extract contextual embedding features from all Camembert layers |
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```python |
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from transformers import CamembertConfig |
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# (Need to reload the model with new config) |
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config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True) |
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camembert = CamembertModel.from_pretrained("camembert-base", config=config) |
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embeddings, _, all_layer_embeddings = camembert(encoded_sentence) |
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# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers) |
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all_layer_embeddings[5] |
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# layer 5 contextual embedding : size torch.Size([1, 10, 768]) |
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#tensor([[[-0.0032, 0.0075, 0.0040, ..., -0.0025, -0.0178, -0.0210], |
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# [-0.0996, -0.1474, 0.1057, ..., -0.0278, 0.1690, -0.2982], |
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# [ 0.0557, -0.0588, 0.0547, ..., -0.0726, -0.0867, 0.0699], |
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# ..., |
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``` |
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