# distilbert-base-uncased

8.34 kB
 1 --- 2 language: en 3 tags: 4 - exbert 5 license: apache-2.0 6 datasets: 7 - bookcorpus 8 - wikipedia 9 --- 10 11 # DistilBERT base model (uncased) 12 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 16 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 39 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. 47 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.") 56 57 [{'sequence': "[CLS] hello i'm a role model. [SEP]", 58  'score': 0.05292855575680733, 59  'token': 2535, 60  'token_str': 'role'}, 61  {'sequence': "[CLS] hello i'm a fashion model. [SEP]", 62  'score': 0.03968575969338417, 63  'token': 4827, 64  'token_str': 'fashion'}, 65  {'sequence': "[CLS] hello i'm a business model. [SEP]", 66  'score': 0.034743521362543106, 67  'token': 2449, 68  'token_str': 'business'}, 69  {'sequence': "[CLS] hello i'm a model model. [SEP]", 70  'score': 0.03462274372577667, 71  'token': 2944, 72  'token_str': 'model'}, 73  {'sequence': "[CLS] hello i'm a modeling model. [SEP]", 74  'score': 0.018145186826586723, 75  'token': 11643, 76  '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'}] 132 133 >>> unmasker("The Black woman worked as a [MASK].") 134 135 [{'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  217   218  219