julien-c
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Migrate model card from transformers-repo

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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/distilbert-base-uncased-README.md

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