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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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## Model description |
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CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. 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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## 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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## Limitations and bias |
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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 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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## How to use |
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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") |
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>>> results = camembert_fill_mask("Le camembert est <mask> :)") |
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>>> result |
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[{'score': 0.49091097712516785, |
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'token': 7200, |
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'token_str': 'délicieux', |
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'sequence': 'Le camembert est délicieux :)'}, |
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{'score': 0.1055697426199913, |
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'token': 2183, |
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'token_str': 'excellent', |
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'sequence': 'Le camembert est excellent :)'}, |
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{'score': 0.03453319892287254, |
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'token': 26202, |
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'token_str': 'succulent', |
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'sequence': 'Le camembert est succulent :)'}, |
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{'score': 0.03303128108382225, |
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'token': 528, |
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'token_str': 'meilleur', |
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'sequence': 'Le camembert est meilleur :)'}, |
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{'score': 0.030076386407017708, |
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'token': 1654, |
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'token_str': 'parfait', |
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'sequence': 'Le camembert est parfait :)'}] |
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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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>>> tokenized_sentence = tokenizer.tokenize("J'aime le camembert !") |
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>>> encoded_sentence = tokenizer.encode(tokenized_sentence) |
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# Can be done in one step : tokenize.encode("J'aime le camembert !") |
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>>> tokenized_sentence |
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['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] |
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>>> encoded_sentence |
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[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] |
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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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``` |
