louismartin Nicolas Fouqué commited on
Commit
e12767c
1 Parent(s): f9c16e8

Create README.md (#1)

Browse files

- Create README.md (a0305af5fbf83114a0980abb4724b0fb4fd7ee62)


Co-authored-by: Nicolas Fouqué <nfouque@users.noreply.huggingface.co>

Files changed (1) hide show
  1. README.md +110 -0
README.md ADDED
@@ -0,0 +1,110 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: fr
3
+ ---
4
+
5
+ # CamemBERT: a Tasty French Language Model
6
+
7
+ ## Introduction
8
+
9
+ [CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
10
+
11
+ 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.
12
+
13
+ For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
14
+
15
+ ## Pre-trained models
16
+
17
+ | Model | #params | Arch. | Training data |
18
+ |--------------------------------|--------------------------------|-------|-----------------------------------|
19
+ | `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
20
+ | `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
21
+ | `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
22
+ | `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
23
+ | `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
24
+ | `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
25
+
26
+ ## How to use CamemBERT with HuggingFace
27
+
28
+ ##### Load CamemBERT and its sub-word tokenizer :
29
+ ```python
30
+ from transformers import CamembertModel, CamembertTokenizer
31
+
32
+ # You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
33
+ tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-wikipedia-4gb")
34
+ camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb")
35
+
36
+ camembert.eval() # disable dropout (or leave in train mode to finetune)
37
+
38
+ ```
39
+
40
+ ##### Filling masks using pipeline
41
+ ```python
42
+ from transformers import pipeline
43
+
44
+ camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-base-wikipedia-4gb", tokenizer="camembert/camembert-base-wikipedia-4gb")
45
+ results = camembert_fill_mask("Le camembert est un fromage de <mask>!")
46
+ # results
47
+ #[{'sequence': '<s> Le camembert est un fromage de chèvre!</s>', 'score': 0.4937814474105835, 'token': 19370},
48
+ #{'sequence': '<s> Le camembert est un fromage de brebis!</s>', 'score': 0.06255942583084106, 'token': 30616},
49
+ #{'sequence': '<s> Le camembert est un fromage de montagne!</s>', 'score': 0.04340197145938873, 'token': 2364},
50
+ # {'sequence': '<s> Le camembert est un fromage de Noël!</s>', 'score': 0.02823255956172943, 'token': 3236},
51
+ #{'sequence': '<s> Le camembert est un fromage de vache!</s>', 'score': 0.021357402205467224, 'token': 12329}]
52
+ ```
53
+
54
+ ##### Extract contextual embedding features from Camembert output
55
+ ```python
56
+ import torch
57
+ # Tokenize in sub-words with SentencePiece
58
+ tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
59
+ # ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!']
60
+
61
+ # 1-hot encode and add special starting and end tokens
62
+ encoded_sentence = tokenizer.encode(tokenized_sentence)
63
+ # [5, 221, 10, 10600, 14, 8952, 10540, 75, 1114, 6]
64
+ # NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
65
+
66
+ # Feed tokens to Camembert as a torch tensor (batch dim 1)
67
+ encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
68
+ embeddings, _ = camembert(encoded_sentence)
69
+ # embeddings.detach()
70
+ # embeddings.size torch.Size([1, 10, 768])
71
+ #tensor([[[-0.0928, 0.0506, -0.0094, ..., -0.2388, 0.1177, -0.1302],
72
+ # [ 0.0662, 0.1030, -0.2355, ..., -0.4224, -0.0574, -0.2802],
73
+ # [-0.0729, 0.0547, 0.0192, ..., -0.1743, 0.0998, -0.2677],
74
+ # ...,
75
+ ```
76
+
77
+ ##### Extract contextual embedding features from all Camembert layers
78
+ ```python
79
+ from transformers import CamembertConfig
80
+ # (Need to reload the model with new config)
81
+ config = CamembertConfig.from_pretrained("camembert/camembert-base-wikipedia-4gb", output_hidden_states=True)
82
+ camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb", config=config)
83
+
84
+ embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
85
+ # all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
86
+ all_layer_embeddings[5]
87
+ # layer 5 contextual embedding : size torch.Size([1, 10, 768])
88
+ #tensor([[[-0.0059, -0.0227, 0.0065, ..., -0.0770, 0.0369, 0.0095],
89
+ # [ 0.2838, -0.1531, -0.3642, ..., -0.0027, -0.8502, -0.7914],
90
+ # [-0.0073, -0.0338, -0.0011, ..., 0.0533, -0.0250, -0.0061],
91
+ # ...,
92
+ ```
93
+
94
+
95
+ ## Authors
96
+
97
+ 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.
98
+
99
+
100
+ ## Citation
101
+ If you use our work, please cite:
102
+
103
+ ```bibtex
104
+ @inproceedings{martin2020camembert,
105
+ title={CamemBERT: a Tasty French Language Model},
106
+ 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},
107
+ booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
108
+ year={2020}
109
+ }
110
+ ```