From 60e1556a44fef5a6b011a88a8251b6bae90c0565 Mon Sep 17 00:00:00 2001 From: Benjamin Muller Date: Wed, 29 Apr 2020 11:42:52 +0800 Subject: [PATCH] Create model_card camembert-base-ccnet-4gb --- .../camembert-base-ccnet-4gb/README.md | 111 ++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100644 model_cards/camembert/camembert-base-ccnet-4gb/README.md diff --git a/model_cards/camembert/camembert-base-ccnet-4gb/README.md b/model_cards/camembert/camembert-base-ccnet-4gb/README.md new file mode 100644 index 0000000000..198ff39412 --- /dev/null +++ b/model_cards/camembert/camembert-base-ccnet-4gb/README.md @@ -0,0 +1,111 @@ +--- +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-base-ccnet-4gb") +camembert = CamembertModel.from_pretrained("camembert/camembert-base-ccnet-4gb") + +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-base-ccnet-4gb", tokenizer="camembert/camembert-base-ccnet-4gb") +results = camembert_fill_mask("Le camembert est-il ?") +# results +#[{'sequence': ' Le camembert est-il sain?', 'score': 0.07001790404319763, 'token': 10286}, +#{'sequence': ' Le camembert est-il français?', 'score': 0.057594332844018936, 'token': 384}, +#{'sequence': ' Le camembert est-il bon?', 'score': 0.04098724573850632, 'token': 305}, +#{'sequence': ' Le camembert est-il périmé?', 'score': 0.03486393392086029, 'token': 30862}, +#{'sequence': ' Le camembert est-il cher?', 'score': 0.021535946056246758, 'token': 1604}] + +``` + +##### 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', '▁ca', 'member', '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() +# embeddings.size torch.Size([1, 10, 768]) +#tensor([[[ 0.0331, 0.0095, -0.2776, ..., 0.2875, -0.0827, -0.2467], +# [-0.1348, 0.0478, -0.5409, ..., 0.8330, 0.0467, 0.0662], +# [ 0.0920, -0.0264, 0.0177, ..., 0.1112, 0.0108, -0.1123], +# ..., +``` + +##### 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-base-ccnet-4gb", output_hidden_states=True) +camembert = CamembertModel.from_pretrained("camembert/camembert-base-ccnet-4gb", config=config) + +embeddings, _, all_layer_embeddings = camembert(encoded_sentence) +# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers) +all_layer_embeddings[5] +# layer 5 contextual embedding : size torch.Size([1, 10, 768]) +#tensor([[[-0.0144, 0.1855, 0.4895, ..., -0.1537, 0.0107, -0.2293], +# [-0.6664, -0.0880, -0.1539, ..., 0.3635, 0.4047, 0.1258], +# [ 0.0511, 0.0540, 0.2545, ..., 0.0709, -0.0288, -0.0779], +# ..., +``` + + +## 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} +} +```