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model_cards/camembert/camembert-large/README.md

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+---
+language: french
+---
+
+# CamemBERT: a Tasty French Language Model
+
+## Introduction
+
+[CamemBERT](https://arxiv.org/abs/1911.03894) 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. 
+
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Pre-trained models
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+
+## How to use CamemBERT with HuggingFace
+
+##### Load CamemBERT and its sub-word tokenizer :
+```python
+from transformers import CamembertModel, CamembertTokenizer
+
+# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
+tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-large")
+camembert = CamembertModel.from_pretrained("camembert/camembert-large")
+
+camembert.eval()  # disable dropout (or leave in train mode to finetune)
+
+```
+
+##### Filling masks using pipeline 
+```python
+from transformers import pipeline 
+
+camembert_fill_mask  = pipeline("fill-mask", model="camembert/camembert-large", tokenizer="camembert/camembert-large")
+results = camembert_fill_mask("Le camembert est <mask> :)")
+# results
+#[{'sequence': '<s> Le camembert est bon :)</s>', 'score': 0.15560828149318695, 'token': 305}, 
+#{'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.06821336597204208, 'token': 3497}, 
+#{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.060438305139541626, 'token': 11661}, 
+#{'sequence': '<s> Le camembert est ici :)</s>', 'score': 0.02023460529744625, 'token': 373}, 
+#{'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.01778135634958744, 'token': 876}]
+```
+
+##### Extract contextual embedding features from Camembert output 
+```python
+import torch
+# Tokenize in sub-words with SentencePiece
+tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
+# ['▁J', "'", 'aime', '▁le', '▁cam', 'ember', 't', '▁!']
+
+# 1-hot encode and add special starting and end tokens 
+encoded_sentence = tokenizer.encode(tokenized_sentence)
+# [5, 133, 22, 1250, 16, 12034, 14324, 81, 76, 6]
+# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
+
+# Feed tokens to Camembert as a torch tensor (batch dim 1)
+encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
+embeddings, _ = camembert(encoded_sentence)
+# embeddings.detach()
+# torch.Size([1, 10, 1024])
+#tensor([[[-0.1284,  0.2643,  0.4374,  ...,  0.1627,  0.1308, -0.2305],
+#         [ 0.4576, -0.6345, -0.2029,  ..., -0.1359, -0.2290, -0.6318],
+#         [ 0.0381,  0.0429,  0.5111,  ..., -0.1177, -0.1913, -0.1121],
+#         ...,
+```
+
+##### Extract contextual embedding features from all Camembert layers
+```python
+from transformers import CamembertConfig
+# (Need to reload the model with new config)
+config = CamembertConfig.from_pretrained("camembert/camembert-large", output_hidden_states=True)
+camembert = CamembertModel.from_pretrained("camembert/camembert-large", config=config)
+
+embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
+#  all_layer_embeddings list of len(all_layer_embeddings) == 25 (input embedding layer + 24 self attention layers)
+all_layer_embeddings[5]
+# layer 5 contextual embedding : size torch.Size([1, 10, 1024])
+#tensor([[[-0.0600,  0.0742,  0.0332,  ..., -0.0525, -0.0637, -0.0287],
+#         [ 0.0950,  0.2840,  0.1985,  ...,  0.2073, -0.2172, -0.6321],
+#         [ 0.1381,  0.1872,  0.1614,  ..., -0.0339, -0.2530, -0.1182],
+#         ...,
+```
+
+
+## Authors 
+
+CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+
+
+## Citation
+If you use our work, please cite:
+
+```bibtex
+@inproceedings{martin2020camembert,
+  title={CamemBERT: a Tasty French Language Model},
+  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},
+  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+  year={2020}
+}
+```