julien-c HF staff commited on
Commit
7e783df
1 Parent(s): 6fe1c53

Migrate model card from transformers-repo

Browse files

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/bert-base-cased-README.md

Files changed (1) hide show
  1. README.md +230 -0
README.md ADDED
@@ -0,0 +1,230 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language: en
3
+ tags:
4
+ - exbert
5
+ license: apache-2.0
6
+ datasets:
7
+ - bookcorpus
8
+ - wikipedia
9
+ ---
10
+
11
+ # BERT base model (cased)
12
+
13
+ Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
14
+ [this paper](https://arxiv.org/abs/1810.04805) and first released in
15
+ [this repository](https://github.com/google-research/bert). This model is case-sensitive: it makes a difference between
16
+ english and English.
17
+
18
+ Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
19
+ the Hugging Face team.
20
+
21
+ ## Model description
22
+
23
+ BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
24
+ was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
25
+ publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
26
+ was pretrained with two objectives:
27
+
28
+ - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
29
+ the entire masked sentence through the model and has to predict the masked words. This is different from traditional
30
+ recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
31
+ GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
32
+ sentence.
33
+ - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
34
+ they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
35
+ predict if the two sentences were following each other or not.
36
+
37
+ This way, the model learns an inner representation of the English language that can then be used to extract features
38
+ useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
39
+ classifier using the features produced by the BERT model as inputs.
40
+
41
+ ## Intended uses & limitations
42
+
43
+ You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
44
+ be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
45
+ fine-tuned versions on a task that interests you.
46
+
47
+ Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
48
+ to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
49
+ generation you should look at model like GPT2.
50
+
51
+ ### How to use
52
+
53
+ You can use this model directly with a pipeline for masked language modeling:
54
+
55
+ ```python
56
+ >>> from transformers import pipeline
57
+ >>> unmasker = pipeline('fill-mask', model='bert-base-cased')
58
+ >>> unmasker("Hello I'm a [MASK] model.")
59
+
60
+ [{'sequence': "[CLS] Hello I'm a fashion model. [SEP]",
61
+ 'score': 0.09019174426794052,
62
+ 'token': 4633,
63
+ 'token_str': 'fashion'},
64
+ {'sequence': "[CLS] Hello I'm a new model. [SEP]",
65
+ 'score': 0.06349995732307434,
66
+ 'token': 1207,
67
+ 'token_str': 'new'},
68
+ {'sequence': "[CLS] Hello I'm a male model. [SEP]",
69
+ 'score': 0.06228214129805565,
70
+ 'token': 2581,
71
+ 'token_str': 'male'},
72
+ {'sequence': "[CLS] Hello I'm a professional model. [SEP]",
73
+ 'score': 0.0441727414727211,
74
+ 'token': 1848,
75
+ 'token_str': 'professional'},
76
+ {'sequence': "[CLS] Hello I'm a super model. [SEP]",
77
+ 'score': 0.03326151892542839,
78
+ 'token': 7688,
79
+ 'token_str': 'super'}]
80
+ ```
81
+
82
+ Here is how to use this model to get the features of a given text in PyTorch:
83
+
84
+ ```python
85
+ from transformers import BertTokenizer, TFBertModel
86
+ tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
87
+ model = TFBertModel.from_pretrained("bert-base-cased")
88
+ text = "Replace me by any text you'd like."
89
+ encoded_input = tokenizer(text, return_tensors='pt')
90
+ output = model(**encoded_input)
91
+ ```
92
+
93
+ and in TensorFlow:
94
+
95
+ ```python
96
+ from transformers import BertTokenizer, BertModel
97
+ tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
98
+ model = BertModel.from_pretrained("bert-base-cased")
99
+ text = "Replace me by any text you'd like."
100
+ encoded_input = tokenizer(text, return_tensors='tf')
101
+ output = model(encoded_input)
102
+ ```
103
+
104
+ ### Limitations and bias
105
+
106
+ Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
107
+ predictions:
108
+
109
+ ```python
110
+ >>> from transformers import pipeline
111
+ >>> unmasker = pipeline('fill-mask', model='bert-base-cased')
112
+ >>> unmasker("The man worked as a [MASK].")
113
+
114
+ [{'sequence': '[CLS] The man worked as a lawyer. [SEP]',
115
+ 'score': 0.04804691672325134,
116
+ 'token': 4545,
117
+ 'token_str': 'lawyer'},
118
+ {'sequence': '[CLS] The man worked as a waiter. [SEP]',
119
+ 'score': 0.037494491785764694,
120
+ 'token': 17989,
121
+ 'token_str': 'waiter'},
122
+ {'sequence': '[CLS] The man worked as a cop. [SEP]',
123
+ 'score': 0.035512614995241165,
124
+ 'token': 9947,
125
+ 'token_str': 'cop'},
126
+ {'sequence': '[CLS] The man worked as a detective. [SEP]',
127
+ 'score': 0.031271643936634064,
128
+ 'token': 9140,
129
+ 'token_str': 'detective'},
130
+ {'sequence': '[CLS] The man worked as a doctor. [SEP]',
131
+ 'score': 0.027423162013292313,
132
+ 'token': 3995,
133
+ 'token_str': 'doctor'}]
134
+
135
+ >>> unmasker("The woman worked as a [MASK].")
136
+
137
+ [{'sequence': '[CLS] The woman worked as a nurse. [SEP]',
138
+ 'score': 0.16927455365657806,
139
+ 'token': 7439,
140
+ 'token_str': 'nurse'},
141
+ {'sequence': '[CLS] The woman worked as a waitress. [SEP]',
142
+ 'score': 0.1501094549894333,
143
+ 'token': 15098,
144
+ 'token_str': 'waitress'},
145
+ {'sequence': '[CLS] The woman worked as a maid. [SEP]',
146
+ 'score': 0.05600163713097572,
147
+ 'token': 13487,
148
+ 'token_str': 'maid'},
149
+ {'sequence': '[CLS] The woman worked as a housekeeper. [SEP]',
150
+ 'score': 0.04838843643665314,
151
+ 'token': 26458,
152
+ 'token_str': 'housekeeper'},
153
+ {'sequence': '[CLS] The woman worked as a cook. [SEP]',
154
+ 'score': 0.029980547726154327,
155
+ 'token': 9834,
156
+ 'token_str': 'cook'}]
157
+ ```
158
+
159
+ This bias will also affect all fine-tuned versions of this model.
160
+
161
+ ## Training data
162
+
163
+ The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
164
+ unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
165
+ headers).
166
+
167
+ ## Training procedure
168
+
169
+ ### Preprocessing
170
+
171
+ The texts are tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form:
172
+
173
+ ```
174
+ [CLS] Sentence A [SEP] Sentence B [SEP]
175
+ ```
176
+
177
+ With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
178
+ the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
179
+ consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
180
+ "sentences" has a combined length of less than 512 tokens.
181
+
182
+ The details of the masking procedure for each sentence are the following:
183
+ - 15% of the tokens are masked.
184
+ - In 80% of the cases, the masked tokens are replaced by `[MASK]`.
185
+ - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
186
+ - In the 10% remaining cases, the masked tokens are left as is.
187
+
188
+ ### Pretraining
189
+
190
+ The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
191
+ of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
192
+ used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
193
+ learning rate warmup for 10,000 steps and linear decay of the learning rate after.
194
+
195
+ ## Evaluation results
196
+
197
+ When fine-tuned on downstream tasks, this model achieves the following results:
198
+
199
+ Glue test results:
200
+
201
+ | Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
202
+ |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
203
+ | | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
204
+
205
+
206
+ ### BibTeX entry and citation info
207
+
208
+ ```bibtex
209
+ @article{DBLP:journals/corr/abs-1810-04805,
210
+ author = {Jacob Devlin and
211
+ Ming{-}Wei Chang and
212
+ Kenton Lee and
213
+ Kristina Toutanova},
214
+ title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
215
+ Understanding},
216
+ journal = {CoRR},
217
+ volume = {abs/1810.04805},
218
+ year = {2018},
219
+ url = {http://arxiv.org/abs/1810.04805},
220
+ archivePrefix = {arXiv},
221
+ eprint = {1810.04805},
222
+ timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
223
+ biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
224
+ bibsource = {dblp computer science bibliography, https://dblp.org}
225
+ }
226
+ ```
227
+
228
+ <a href="https://huggingface.co/exbert/?model=bert-base-cased">
229
+ <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
230
+ </a>