Update code samples & dimensions

README.md

@@ -1,3 +1,262 @@
 ---
+language: en
 license: apache-2.0
+datasets:
+- bookcorpus
+- wikipedia
 ---
+
+# BERT large model (cased)
+
+Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://arxiv.org/abs/1810.04805) and first released in
+[this repository](https://github.com/google-research/bert). This model is cased: it makes a difference
+between english and English.
+
+Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
+the Hugging Face team.
+
+## Model description
+
+BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. 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. More precisely, it
+was pretrained with two objectives:
+
+- Masked language modeling (MLM): 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.
+- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
+  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
+  predict if the two sentences were following each other or not.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
+classifier using the features produced by the BERT model as inputs.
+
+This model has the following configuration:
+
+- 24-layer
+- 1024 hidden dimension
+- 16 attention heads
+- 336M parameters.
+
+## 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=bert) 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='bert-large-cased')
+>>> unmasker("Hello I'm a [MASK] model.")
+[
+   {
+      "sequence":"[CLS] Hello I'm a male model. [SEP]",
+      "score":0.22748498618602753,
+      "token":2581,
+      "token_str":"male"
+   },
+   {
+      "sequence":"[CLS] Hello I'm a fashion model. [SEP]",
+      "score":0.09146175533533096,
+      "token":4633,
+      "token_str":"fashion"
+   },
+   {
+      "sequence":"[CLS] Hello I'm a new model. [SEP]",
+      "score":0.05823173746466637,
+      "token":1207,
+      "token_str":"new"
+   },
+   {
+      "sequence":"[CLS] Hello I'm a super model. [SEP]",
+      "score":0.04488750174641609,
+      "token":7688,
+      "token_str":"super"
+   },
+   {
+      "sequence":"[CLS] Hello I'm a famous model. [SEP]",
+      "score":0.03271442651748657,
+      "token":2505,
+      "token_str":"famous"
+   }
+]
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+from transformers import BertTokenizer, BertModel
+tokenizer = BertTokenizer.from_pretrained('bert-large-cased')
+model = BertModel.from_pretrained("bert-large-cased")
+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 BertTokenizer, TFBertModel
+tokenizer = BertTokenizer.from_pretrained('bert-large-cased')
+model = TFBertModel.from_pretrained("bert-large-cased")
+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:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='bert-large-cased')
+>>> unmasker("The man worked as a [MASK].")
+[
+   {
+      "sequence":"[CLS] The man worked as a doctor. [SEP]",
+      "score":0.0645911768078804,
+      "token":3995,
+      "token_str":"doctor"
+   },
+   {
+      "sequence":"[CLS] The man worked as a cop. [SEP]",
+      "score":0.057450827211141586,
+      "token":9947,
+      "token_str":"cop"
+   },
+   {
+      "sequence":"[CLS] The man worked as a mechanic. [SEP]",
+      "score":0.04392256215214729,
+      "token":19459,
+      "token_str":"mechanic"
+   },
+   {
+      "sequence":"[CLS] The man worked as a waiter. [SEP]",
+      "score":0.03755280375480652,
+      "token":17989,
+      "token_str":"waiter"
+   },
+   {
+      "sequence":"[CLS] The man worked as a teacher. [SEP]",
+      "score":0.03458863124251366,
+      "token":3218,
+      "token_str":"teacher"
+   }
+]
+
+>>> unmasker("The woman worked as a [MASK].")
+[
+   {
+      "sequence":"[CLS] The woman worked as a nurse. [SEP]",
+      "score":0.2572779953479767,
+      "token":7439,
+      "token_str":"nurse"
+   },
+   {
+      "sequence":"[CLS] The woman worked as a waitress. [SEP]",
+      "score":0.16706500947475433,
+      "token":15098,
+      "token_str":"waitress"
+   },
+   {
+      "sequence":"[CLS] The woman worked as a teacher. [SEP]",
+      "score":0.04587847739458084,
+      "token":3218,
+      "token_str":"teacher"
+   },
+   {
+      "sequence":"[CLS] The woman worked as a secretary. [SEP]",
+      "score":0.03577028587460518,
+      "token":4848,
+      "token_str":"secretary"
+   },
+   {
+      "sequence":"[CLS] The woman worked as a maid. [SEP]",
+      "score":0.03298963978886604,
+      "token":13487,
+      "token_str":"maid"
+   }
+]
+```
+
+This bias will also affect all fine-tuned versions of this model.
+
+## Training data
+
+The BERT model was pretrained on [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 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
+of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
+used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
+learning rate warmup for 10,000 steps and linear decay of the learning rate after.
+
+## Evaluation results
+
+When fine-tuned on downstream tasks, this model achieves the following results:
+
+Model                                    | SQUAD 1.1 F1/EM | Multi NLI Accuracy
+---------------------------------------- | :-------------: | :----------------:
+BERT-Large, Cased (Original)             | 91.5/84.8       | 86.09
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-1810-04805,
+  author    = {Jacob Devlin and
+               Ming{-}Wei Chang and
+               Kenton Lee and
+               Kristina Toutanova},
+  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
+               Understanding},
+  journal   = {CoRR},
+  volume    = {abs/1810.04805},
+  year      = {2018},
+  url       = {http://arxiv.org/abs/1810.04805},
+  archivePrefix = {arXiv},
+  eprint    = {1810.04805},
+  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```