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README.md

@@ -1,230 +0,0 @@
----
-language: en
-tags:
-- exbert
-license: apache-2.0
-datasets:
-- bookcorpus
-- wikipedia
----
-
-# BERT base 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 case-sensitive: 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.
-
-## 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-base-cased')
->>> unmasker("Hello I'm a [MASK] model.")
-
-[{'sequence': "[CLS] Hello I'm a fashion model. [SEP]",
-  'score': 0.09019174426794052,
-  'token': 4633,
-  'token_str': 'fashion'},
- {'sequence': "[CLS] Hello I'm a new model. [SEP]",
-  'score': 0.06349995732307434,
-  'token': 1207,
-  'token_str': 'new'},
- {'sequence': "[CLS] Hello I'm a male model. [SEP]",
-  'score': 0.06228214129805565,
-  'token': 2581,
-  'token_str': 'male'},
- {'sequence': "[CLS] Hello I'm a professional model. [SEP]",
-  'score': 0.0441727414727211,
-  'token': 1848,
-  'token_str': 'professional'},
- {'sequence': "[CLS] Hello I'm a super model. [SEP]",
-  'score': 0.03326151892542839,
-  'token': 7688,
-  'token_str': 'super'}]
-```
-
-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-base-cased')
-model = BertModel.from_pretrained("bert-base-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-base-cased')
-model = TFBertModel.from_pretrained("bert-base-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-base-cased')
->>> unmasker("The man worked as a [MASK].")
-
-[{'sequence': '[CLS] The man worked as a lawyer. [SEP]',
-  'score': 0.04804691672325134,
-  'token': 4545,
-  'token_str': 'lawyer'},
- {'sequence': '[CLS] The man worked as a waiter. [SEP]',
-  'score': 0.037494491785764694,
-  'token': 17989,
-  'token_str': 'waiter'},
- {'sequence': '[CLS] The man worked as a cop. [SEP]',
-  'score': 0.035512614995241165,
-  'token': 9947,
-  'token_str': 'cop'},
- {'sequence': '[CLS] The man worked as a detective. [SEP]',
-  'score': 0.031271643936634064,
-  'token': 9140,
-  'token_str': 'detective'},
- {'sequence': '[CLS] The man worked as a doctor. [SEP]',
-  'score': 0.027423162013292313,
-  'token': 3995,
-  'token_str': 'doctor'}]
-
->>> unmasker("The woman worked as a [MASK].")
-
-[{'sequence': '[CLS] The woman worked as a nurse. [SEP]',
-  'score': 0.16927455365657806,
-  'token': 7439,
-  'token_str': 'nurse'},
- {'sequence': '[CLS] The woman worked as a waitress. [SEP]',
-  'score': 0.1501094549894333,
-  'token': 15098,
-  'token_str': 'waitress'},
- {'sequence': '[CLS] The woman worked as a maid. [SEP]',
-  'score': 0.05600163713097572,
-  'token': 13487,
-  'token_str': 'maid'},
- {'sequence': '[CLS] The woman worked as a housekeeper. [SEP]',
-  'score': 0.04838843643665314,
-  'token': 26458,
-  'token_str': 'housekeeper'},
- {'sequence': '[CLS] The woman worked as a cook. [SEP]',
-  'score': 0.029980547726154327,
-  'token': 9834,
-  'token_str': 'cook'}]
-```
-
-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 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:
-
-Glue test results:
-
-| Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
-|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
-|      | 84.6/83.4   | 71.2 | 90.5 | 93.5  | 52.1 | 85.8  | 88.9 | 66.4 | 79.6    |
-
-
-### 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}
-}
-```
-
-<a href="https://huggingface.co/exbert/?model=bert-base-cased">
-	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
-</a>

config.json

@@ -3,7 +3,6 @@
     "BertForMaskedLM"
   ],
   "attention_probs_dropout_prob": 0.1,
-  "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
@@ -15,9 +14,6 @@
   "num_attention_heads": 12,
   "num_hidden_layers": 12,
   "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "transformers_version": "4.6.0.dev0",
   "type_vocab_size": 2,
-  "use_cache": true,
   "vocab_size": 28996
 }

modelcard.json

@@ -0,0 +1,11 @@
+{
+  "caveats_and_recommendations": {},
+  "ethical_considerations": {},
+  "evaluation_data": {},
+  "factors": {},
+  "intended_use": {},
+  "metrics": {},
+  "model_details": {},
+  "quantitative_analyses": {},
+  "training_data": {}
+}