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README.md

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+---
+language: en
+tags:
+- exbert
+license: apache-2.0
+datasets:
+- bookcorpus
+- wikipedia
+---
+
+# DistilBERT base model (uncased)
+
+This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was
+introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found
+[here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does
+not make a difference between english and English.
+
+## Model description
+
+DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a
+self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only,
+with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic
+process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained
+with three objectives:
+
+- Distillation loss: the model was trained to return the same probabilities as the BERT base model.
+- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a
+  sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the
+  model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that
+  usually see the words one after the other, or from autoregressive models like GPT which internally mask the future
+  tokens. It allows the model to learn a bidirectional representation of the sentence.
+- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base
+  model.
+
+This way, the model learns the same inner representation of the English language than its teacher model, while being
+faster for inference or downstream tasks.
+
+## Intended uses & limitations
+
+You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
+be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for
+fine-tuned versions on a task that interests you.
+
+Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
+to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
+generation you should look at model like GPT2.
+
+### How to use
+
+You can use this model directly with a pipeline for masked language modeling:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
+>>> unmasker("Hello I'm a [MASK] model.")
+
+[{'sequence': "[CLS] hello i'm a role model. [SEP]",
+  'score': 0.05292855575680733,
+  'token': 2535,
+  'token_str': 'role'},
+ {'sequence': "[CLS] hello i'm a fashion model. [SEP]",
+  'score': 0.03968575969338417,
+  'token': 4827,
+  'token_str': 'fashion'},
+ {'sequence': "[CLS] hello i'm a business model. [SEP]",
+  'score': 0.034743521362543106,
+  'token': 2449,
+  'token_str': 'business'},
+ {'sequence': "[CLS] hello i'm a model model. [SEP]",
+  'score': 0.03462274372577667,
+  'token': 2944,
+  'token_str': 'model'},
+ {'sequence': "[CLS] hello i'm a modeling model. [SEP]",
+  'score': 0.018145186826586723,
+  'token': 11643,
+  'token_str': 'modeling'}]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import DistilBertTokenizer, DistilBertModel
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertModel.from_pretrained("distilbert-base-uncased")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+```
+
+and in TensorFlow:
+
+```python
+from transformers import DistilBertTokenizer, TFDistilBertModel
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
+text = "Replace me by any text you'd like."
+encoded_input = tokenizer(text, return_tensors='tf')
+output = model(encoded_input)
+```
+
+### Limitations and bias
+
+Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
+predictions. It also inherits some of
+[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias).
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased')
+>>> unmasker("The White man worked as a [MASK].")
+
+[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]',
+  'score': 0.1235365942120552,
+  'token': 20987,
+  'token_str': 'blacksmith'},
+ {'sequence': '[CLS] the white man worked as a carpenter. [SEP]',
+  'score': 0.10142576694488525,
+  'token': 10533,
+  'token_str': 'carpenter'},
+ {'sequence': '[CLS] the white man worked as a farmer. [SEP]',
+  'score': 0.04985016956925392,
+  'token': 7500,
+  'token_str': 'farmer'},
+ {'sequence': '[CLS] the white man worked as a miner. [SEP]',
+  'score': 0.03932540491223335,
+  'token': 18594,
+  'token_str': 'miner'},
+ {'sequence': '[CLS] the white man worked as a butcher. [SEP]',
+  'score': 0.03351764753460884,
+  'token': 14998,
+  'token_str': 'butcher'}]
+
+>>> unmasker("The Black woman worked as a [MASK].")
+
+[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]',
+  'score': 0.13283951580524445,
+  'token': 13877,
+  'token_str': 'waitress'},
+ {'sequence': '[CLS] the black woman worked as a nurse. [SEP]',
+  'score': 0.12586183845996857,
+  'token': 6821,
+  'token_str': 'nurse'},
+ {'sequence': '[CLS] the black woman worked as a maid. [SEP]',
+  'score': 0.11708822101354599,
+  'token': 10850,
+  'token_str': 'maid'},
+ {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]',
+  'score': 0.11499975621700287,
+  'token': 19215,
+  'token_str': 'prostitute'},
+ {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]',
+  'score': 0.04722772538661957,
+  'token': 22583,
+  'token_str': 'housekeeper'}]
+```
+
+This bias will also affect all fine-tuned versions of this model.
+
+## Training data
+
+DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset
+consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia)
+(excluding lists, tables and headers).
+
+## Training procedure
+
+### Preprocessing
+
+The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
+then of the form:
+
+```
+[CLS] Sentence A [SEP] Sentence B [SEP]
+```
+
+With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
+the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
+consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
+"sentences" has a combined length of less than 512 tokens.
+
+The details of the masking procedure for each sentence are the following:
+- 15% of the tokens are masked.
+- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
+- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
+- In the 10% remaining cases, the masked tokens are left as is.
+
+### Pretraining
+
+The model was trained on 8 16 GB V100 for 90 hours. See the
+[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters
+details.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, this model achieves the following results:
+
+Glue test results:
+
+| Task | MNLI | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  |
+|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|
+|      | 82.2 | 88.5 | 89.2 | 91.3  | 51.3 | 85.8  | 87.5 | 59.9 |
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{Sanh2019DistilBERTAD,
+  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
+  author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1910.01108}
+}
+```
+
+<a href="https://huggingface.co/exbert/?model=distilbert-base-uncased">
+	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
+</a>

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17",
+    "18": "LABEL_18",
+    "19": "LABEL_19",
+    "20": "LABEL_20",
+    "21": "LABEL_21",
+    "22": "LABEL_22",
+    "23": "LABEL_23",
+    "24": "LABEL_24",
+    "25": "LABEL_25",
+    "26": "LABEL_26",
+    "27": "LABEL_27",
+    "28": "LABEL_28",
+    "29": "LABEL_29",
+    "30": "LABEL_30",
+    "31": "LABEL_31",
+    "32": "LABEL_32",
+    "33": "LABEL_33",
+    "34": "LABEL_34",
+    "35": "LABEL_35",
+    "36": "LABEL_36",
+    "37": "LABEL_37"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_15": 15,
+    "LABEL_16": 16,
+    "LABEL_17": 17,
+    "LABEL_18": 18,
+    "LABEL_19": 19,
+    "LABEL_2": 2,
+    "LABEL_20": 20,
+    "LABEL_21": 21,
+    "LABEL_22": 22,
+    "LABEL_23": 23,
+    "LABEL_24": 24,
+    "LABEL_25": 25,
+    "LABEL_26": 26,
+    "LABEL_27": 27,
+    "LABEL_28": 28,
+    "LABEL_29": 29,
+    "LABEL_3": 3,
+    "LABEL_30": 30,
+    "LABEL_31": 31,
+    "LABEL_32": 32,
+    "LABEL_33": 33,
+    "LABEL_34": 34,
+    "LABEL_35": 35,
+    "LABEL_36": 36,
+    "LABEL_37": 37,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "vocab_size": 30522
+}

label_map.txt

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+Statement-non-opinion|sd
+Acknowledge (Backchannel)|b
+Statement-opinion|sv
+Agree/Accept|aa
+Appreciation|ba
+Yes-No-Question|qy
+Yes Answers|ny
+Conventional-closing|fc
+Wh-Question|qw
+No Answers|nn
+Response Acknowledgement|bk
+Hedge|h
+Declarative Yes-No-Question|qy^d
+Backchannel in Question Form|bh
+Quotation|^q
+Summarize/Reformulate|bf
+Other|fo_o_fw_"_by_bc
+Affirmative Non-yes Answers|na
+Action-directive|ad
+Collaborative Completion|^2
+Repeat-phrase|b^m
+Open-Question|qo
+Rhetorical-Question|qh
+Hold Before Answer/Agreement|^h
+Negative Non-no Answers|ng
+Signal-non-understanding|br
+Conventional-opening|fp
+Or-Clause|qrr
+Dispreferred Answers|arp_nd
+3rd-party-talk|t3
+Offers, Options Commits|oo_co_cc
+Maybe/Accept-part|aap_am
+Downplayer|bd
+Self-talk|t1
+Tag-Question|^g
+Declarative Wh-Question|qw^d
+Apology|fa
+Thanking|ft

tf_model.h5

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+oid sha256:ad281d92305e39a5b9999a60c593f74b5e172fe5cc77630a47d05ba75fd4917c
+size 268063304

tokenizer_config.json

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+{
+  "do_lower_case": true
+}