From 1851a64b6f19c2c034c2ca9a0e7df5a382b76913 Mon Sep 17 00:00:00 2001 From: Benjamin Muller Date: Wed, 29 Apr 2020 11:45:34 +0800 Subject: [PATCH] create model_card camembert-base-wikipedia-4gb --- .../camembert-base-wikipedia-4gb/README.md | 110 ++++++++++++++++++ 1 file changed, 110 insertions(+) create mode 100644 model_cards/camembert/camembert-base-wikipedia-4gb/README.md diff --git a/model_cards/camembert/camembert-base-wikipedia-4gb/README.md b/model_cards/camembert/camembert-base-wikipedia-4gb/README.md new file mode 100644 index 0000000000..e5775b38e8 --- /dev/null +++ b/model_cards/camembert/camembert-base-wikipedia-4gb/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-base-wikipedia-4gb") +camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-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-wikipedia-4gb", tokenizer="camembert/camembert-base-wikipedia-4gb") +results = camembert_fill_mask("Le camembert est un fromage de !") +# results +#[{'sequence': ' Le camembert est un fromage de chèvre!', 'score': 0.4937814474105835, 'token': 19370}, +#{'sequence': ' Le camembert est un fromage de brebis!', 'score': 0.06255942583084106, 'token': 30616}, +#{'sequence': ' Le camembert est un fromage de montagne!', 'score': 0.04340197145938873, 'token': 2364}, +# {'sequence': ' Le camembert est un fromage de Noël!', 'score': 0.02823255956172943, 'token': 3236}, +#{'sequence': ' Le camembert est un fromage de vache!', 'score': 0.021357402205467224, 'token': 12329}] +``` + +##### 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, 221, 10, 10600, 14, 8952, 10540, 75, 1114, 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([[[-0.0928, 0.0506, -0.0094, ..., -0.2388, 0.1177, -0.1302], +# [ 0.0662, 0.1030, -0.2355, ..., -0.4224, -0.0574, -0.2802], +# [-0.0729, 0.0547, 0.0192, ..., -0.1743, 0.0998, -0.2677], +# ..., +``` + +##### 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-wikipedia-4gb", output_hidden_states=True) +camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-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([[[-0.0059, -0.0227, 0.0065, ..., -0.0770, 0.0369, 0.0095], +# [ 0.2838, -0.1531, -0.3642, ..., -0.0027, -0.8502, -0.7914], +# [-0.0073, -0.0338, -0.0011, ..., 0.0533, -0.0250, -0.0061], +# ..., +``` + + +## 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} +} +```