From afe002b04cebafb25ab90f3fee24d7e1c41b311f Mon Sep 17 00:00:00 2001 From: Benjamin Muller Date: Wed, 29 Apr 2020 11:31:27 +0800 Subject: [PATCH] Create README.md --- .../camembert/camembert-large/README.md | 110 ++++++++++++++++++ 1 file changed, 110 insertions(+) create mode 100644 model_cards/camembert/camembert-large/README.md diff --git a/model_cards/camembert/camembert-large/README.md b/model_cards/camembert/camembert-large/README.md new file mode 100644 index 0000000000..a1a460f9e9 --- /dev/null +++ b/model_cards/camembert/camembert-large/README.md @@ -0,0 +1,110 @@ +--- +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 :)") +# results +#[{'sequence': ' Le camembert est bon :)', 'score': 0.15560828149318695, 'token': 305}, +#{'sequence': ' Le camembert est excellent :)', 'score': 0.06821336597204208, 'token': 3497}, +#{'sequence': ' Le camembert est délicieux :)', 'score': 0.060438305139541626, 'token': 11661}, +#{'sequence': ' Le camembert est ici :)', 'score': 0.02023460529744625, 'token': 373}, +#{'sequence': ' Le camembert est meilleur :)', '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} +} +```