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  3. config.json +22 -0
  4. flax_model.msgpack +3 -0
  5. pytorch_model.bin +3 -0
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README.md ADDED
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+ ---
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+ language: en
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+ tags:
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+ - exbert
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+ license: apache-2.0
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+ datasets:
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+ - bookcorpus
8
+ - wikipedia
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+ ---
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+
11
+ # DistilBERT base model (uncased)
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+
13
+ This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
14
+ introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
15
+ [here](https://github.com/huggingface/transformers/tree/master/examples/distillation). This model is uncased: it does
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+ not make a difference between english and English.
17
+
18
+ ## Model description
19
+
20
+ DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
21
+ self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
22
+ with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
23
+ process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
24
+ with three objectives:
25
+
26
+ - Distillation loss: the model was trained to return the same probabilities as the BERT base model.
27
+ - Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
28
+ sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
29
+ model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
30
+ usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
31
+ tokens. It allows the model to learn a bidirectional representation of the sentence.
32
+ - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
33
+ model.
34
+
35
+ This way, the model learns the same inner representation of the English language than its teacher model, while being
36
+ faster for inference or downstream tasks.
37
+
38
+ ## Intended uses & limitations
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+
40
+ You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
41
+ be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for
42
+ fine-tuned versions on a task that interests you.
43
+
44
+ Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
45
+ to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
46
+ generation you should look at model like GPT2.
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+
48
+ ### How to use
49
+
50
+ You can use this model directly with a pipeline for masked language modeling:
51
+
52
+ ```python
53
+ >>> from transformers import pipeline
54
+ >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
55
+ >>> unmasker("Hello I'm a [MASK] model.")
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+
57
+ [{'sequence': "[CLS] hello i'm a role model. [SEP]",
58
+ 'score': 0.05292855575680733,
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+ 'token': 2535,
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+ 'token_str': 'role'},
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+ {'sequence': "[CLS] hello i'm a fashion model. [SEP]",
62
+ 'score': 0.03968575969338417,
63
+ 'token': 4827,
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+ 'token_str': 'fashion'},
65
+ {'sequence': "[CLS] hello i'm a business model. [SEP]",
66
+ 'score': 0.034743521362543106,
67
+ 'token': 2449,
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+ 'token_str': 'business'},
69
+ {'sequence': "[CLS] hello i'm a model model. [SEP]",
70
+ 'score': 0.03462274372577667,
71
+ 'token': 2944,
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+ 'token_str': 'model'},
73
+ {'sequence': "[CLS] hello i'm a modeling model. [SEP]",
74
+ 'score': 0.018145186826586723,
75
+ 'token': 11643,
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+ 'token_str': 'modeling'}]
77
+ ```
78
+
79
+ Here is how to use this model to get the features of a given text in PyTorch:
80
+
81
+ ```python
82
+ from transformers import DistilBertTokenizer, DistilBertModel
83
+ tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
84
+ model = DistilBertModel.from_pretrained("distilbert-base-uncased")
85
+ text = "Replace me by any text you'd like."
86
+ encoded_input = tokenizer(text, return_tensors='pt')
87
+ output = model(**encoded_input)
88
+ ```
89
+
90
+ and in TensorFlow:
91
+
92
+ ```python
93
+ from transformers import DistilBertTokenizer, TFDistilBertModel
94
+ tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
95
+ model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
96
+ text = "Replace me by any text you'd like."
97
+ encoded_input = tokenizer(text, return_tensors='tf')
98
+ output = model(encoded_input)
99
+ ```
100
+
101
+ ### Limitations and bias
102
+
103
+ Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
104
+ predictions. It also inherits some of
105
+ [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
106
+
107
+ ```python
108
+ >>> from transformers import pipeline
109
+ >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
110
+ >>> unmasker("The White man worked as a [MASK].")
111
+
112
+ [{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]',
113
+ 'score': 0.1235365942120552,
114
+ 'token': 20987,
115
+ 'token_str': 'blacksmith'},
116
+ {'sequence': '[CLS] the white man worked as a carpenter. [SEP]',
117
+ 'score': 0.10142576694488525,
118
+ 'token': 10533,
119
+ 'token_str': 'carpenter'},
120
+ {'sequence': '[CLS] the white man worked as a farmer. [SEP]',
121
+ 'score': 0.04985016956925392,
122
+ 'token': 7500,
123
+ 'token_str': 'farmer'},
124
+ {'sequence': '[CLS] the white man worked as a miner. [SEP]',
125
+ 'score': 0.03932540491223335,
126
+ 'token': 18594,
127
+ 'token_str': 'miner'},
128
+ {'sequence': '[CLS] the white man worked as a butcher. [SEP]',
129
+ 'score': 0.03351764753460884,
130
+ 'token': 14998,
131
+ 'token_str': 'butcher'}]
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+
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+ >>> unmasker("The Black woman worked as a [MASK].")
134
+
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+ [{'sequence': '[CLS] the black woman worked as a waitress. [SEP]',
136
+ 'score': 0.13283951580524445,
137
+ 'token': 13877,
138
+ 'token_str': 'waitress'},
139
+ {'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
140
+ 'score': 0.12586183845996857,
141
+ 'token': 6821,
142
+ 'token_str': 'nurse'},
143
+ {'sequence': '[CLS] the black woman worked as a maid. [SEP]',
144
+ 'score': 0.11708822101354599,
145
+ 'token': 10850,
146
+ 'token_str': 'maid'},
147
+ {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]',
148
+ 'score': 0.11499975621700287,
149
+ 'token': 19215,
150
+ 'token_str': 'prostitute'},
151
+ {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]',
152
+ 'score': 0.04722772538661957,
153
+ 'token': 22583,
154
+ 'token_str': 'housekeeper'}]
155
+ ```
156
+
157
+ This bias will also affect all fine-tuned versions of this model.
158
+
159
+ ## Training data
160
+
161
+ DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
162
+ consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
163
+ (excluding lists, tables and headers).
164
+
165
+ ## Training procedure
166
+
167
+ ### Preprocessing
168
+
169
+ The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
170
+ then of the form:
171
+
172
+ ```
173
+ [CLS] Sentence A [SEP] Sentence B [SEP]
174
+ ```
175
+
176
+ With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
177
+ the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
178
+ consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
179
+ "sentences" has a combined length of less than 512 tokens.
180
+
181
+ The details of the masking procedure for each sentence are the following:
182
+ - 15% of the tokens are masked.
183
+ - In 80% of the cases, the masked tokens are replaced by `[MASK]`.
184
+ - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
185
+ - In the 10% remaining cases, the masked tokens are left as is.
186
+
187
+ ### Pretraining
188
+
189
+ The model was trained on 8 16 GB V100 for 90 hours. See the
190
+ [training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters
191
+ details.
192
+
193
+ ## Evaluation results
194
+
195
+ When fine-tuned on downstream tasks, this model achieves the following results:
196
+
197
+ Glue test results:
198
+
199
+ | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE |
200
+ |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
201
+ | | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 |
202
+
203
+
204
+ ### BibTeX entry and citation info
205
+
206
+ ```bibtex
207
+ @article{Sanh2019DistilBERTAD,
208
+ title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
209
+ author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
210
+ journal={ArXiv},
211
+ year={2019},
212
+ volume={abs/1910.01108}
213
+ }
214
+ ```
215
+
216
+ <a href="https://huggingface.co/exbert/?model=distilbert-base-uncased">
217
+ <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
218
+ </a>
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+ "DistilBertForMaskedLM"
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+ ],
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+ "attention_dropout": 0.1,
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+ "dim": 768,
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+ "hidden_dim": 3072,
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+ "initializer_range": 0.02,
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+ "max_position_embeddings": 512,
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+ "model_type": "distilbert",
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+ "n_heads": 12,
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+ "n_layers": 6,
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+ "pad_token_id": 0,
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+ "qa_dropout": 0.1,
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+ "seq_classif_dropout": 0.2,
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+ "sinusoidal_pos_embds": false,
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+ "tie_weights_": true,
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+ "transformers_version": "4.10.0.dev0",
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+ "vocab_size": 30522
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+ }
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tokenizer_config.json ADDED
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+ {
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+ "do_lower_case": true
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+ }
vocab.txt ADDED
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