Migrate model card from transformers-repo

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

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/master/examples/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>