Copy model card from bert-large-uncased-whole-word-masking

README.md

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+---
+language: en
+license: apache-2.0
+datasets:
+- bookcorpus
+- wikipedia
+---
+
+# BERT large model (cased) whole word masking
+
+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 does not make a difference
+between english and English.
+
+Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same.
+
+The training is identical -- each masked WordPiece token is predicted independently. 
+
+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.
+
+## 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-whole-word-masking')
+>>> unmasker("Hello I'm a [MASK] model.")
+[
+    {
+        'sequence': "[CLS] hello i'm a fashion model. [SEP]", 
+        'score': 0.15813860297203064, 
+        'token': 4827, 
+        'token_str': 'fashion'
+    }, {
+        'sequence': "[CLS] hello i'm a cover model. [SEP]", 
+        'score': 0.10551052540540695, 
+        'token': 3104, 
+        'token_str': 'cover'
+    }, {
+        'sequence': "[CLS] hello i'm a male model. [SEP]", 
+        'score': 0.08340442180633545, 
+        'token': 3287, 
+        'token_str': 'male'
+    }, {
+        'sequence': "[CLS] hello i'm a super model. [SEP]", 
+        'score': 0.036381796002388, 
+        'token': 3565, 
+        'token_str': 'super'
+    }, {
+        'sequence': "[CLS] hello i'm a top model. [SEP]", 
+        'score': 0.03609578311443329, 
+        'token': 2327, 
+        'token_str': 'top'
+    }
+]
+```
+
+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-whole-word-masking')
+model = BertModel.from_pretrained("bert-large-cased-whole-word-masking")
+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-whole-word-masking')
+model = TFBertModel.from_pretrained("bert-large-cased-whole-word-masking")
+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-whole-word-masking')
+>>> unmasker("The man worked as a [MASK].")
+[
+   {
+      "sequence":"[CLS] the man worked as a waiter. [SEP]",
+      "score":0.09823174774646759,
+      "token":15610,
+      "token_str":"waiter"
+   },
+   {
+      "sequence":"[CLS] the man worked as a carpenter. [SEP]",
+      "score":0.08976428955793381,
+      "token":10533,
+      "token_str":"carpenter"
+   },
+   {
+      "sequence":"[CLS] the man worked as a mechanic. [SEP]",
+      "score":0.06550426036119461,
+      "token":15893,
+      "token_str":"mechanic"
+   },
+   {
+      "sequence":"[CLS] the man worked as a butcher. [SEP]",
+      "score":0.04142395779490471,
+      "token":14998,
+      "token_str":"butcher"
+   },
+   {
+      "sequence":"[CLS] the man worked as a barber. [SEP]",
+      "score":0.03680137172341347,
+      "token":13362,
+      "token_str":"barber"
+   }
+]
+
+>>> unmasker("The woman worked as a [MASK].")
+[
+   {
+      "sequence":"[CLS] the woman worked as a waitress. [SEP]",
+      "score":0.2669651508331299,
+      "token":13877,
+      "token_str":"waitress"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a maid. [SEP]",
+      "score":0.13054853677749634,
+      "token":10850,
+      "token_str":"maid"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a nurse. [SEP]",
+      "score":0.07987703382968903,
+      "token":6821,
+      "token_str":"nurse"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a prostitute. [SEP]",
+      "score":0.058545831590890884,
+      "token":19215,
+      "token_str":"prostitute"
+   },
+   {
+      "sequence":"[CLS] the woman worked as a cleaner. [SEP]",
+      "score":0.03834161534905434,
+      "token":20133,
+      "token_str":"cleaner"
+   }
+]
+```
+
+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, Uncased (Whole Word Masking) | 92.8/86.7       | 87.07
+
+
+### 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}
+}
+```