Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- prithivMLmods/Betelgeuse-bert-base-uncased
+pipeline_tag: fill-mask
+library_name: transformers
+---
+
+# BETELGEUSE BERT BASED UNCASED
+![row01](BERT.png)
+## 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 labeling 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 masks 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.
+
+## Model variations
+
+BERT has originally been released in base and large variations, for cased and uncased input text. The uncased models also strips out an accent markers.  
+Chinese and multilingual uncased and cased versions followed shortly after.  
+Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models.  
+Other 24 smaller models are released afterward.  
+
+## 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 of 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='prithivMLmods/Betelgeuse-bert-base-uncased')
+>>> unmasker("Hello I'm a [MASK] model.")
+
+[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
+  'score': 0.1073106899857521,
+  'token': 4827,
+  'token_str': 'fashion'},
+ {'sequence': "[CLS] hello i'm a role model. [SEP]",
+  'score': 0.08774490654468536,
+  'token': 2535,
+  'token_str': 'role'},
+ {'sequence': "[CLS] hello i'm a new model. [SEP]",
+  'score': 0.05338378623127937,
+  'token': 2047,
+  'token_str': 'new'},
+ {'sequence': "[CLS] hello i'm a super model. [SEP]",
+  'score': 0.04667217284440994,
+  'token': 3565,
+  'token_str': 'super'},
+ {'sequence': "[CLS] hello i'm a fine model. [SEP]",
+  'score': 0.027095865458250046,
+  'token': 2986,
+  'token_str': 'fine'}]
+```
+
+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('prithivMLmods/Betelgeuse-bert-base-uncased')
+model = BertModel.from_pretrained("prithivMLmods/Betelgeuse-bert-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 BertTokenizer, TFBertModel
+tokenizer = BertTokenizer.from_pretrained('prithivMLmods/Betelgeuse-bert-base-uncased')
+model = TFBertModel.from_pretrained("prithivMLmods/Betelgeuse-bert-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:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='prithivMLmods/Betelgeuse-bert-base-uncased')
+>>> unmasker("The man worked as a [MASK].")
+
+[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
+  'score': 0.09747550636529922,
+  'token': 10533,
+  'token_str': 'carpenter'},
+ {'sequence': '[CLS] the man worked as a waiter. [SEP]',
+  'score': 0.0523831807076931,
+  'token': 15610,
+  'token_str': 'waiter'},
+ {'sequence': '[CLS] the man worked as a barber. [SEP]',
+  'score': 0.04962705448269844,
+  'token': 13362,
+  'token_str': 'barber'},
+ {'sequence': '[CLS] the man worked as a mechanic. [SEP]',
+  'score': 0.03788609802722931,
+  'token': 15893,
+  'token_str': 'mechanic'},
+ {'sequence': '[CLS] the man worked as a salesman. [SEP]',
+  'score': 0.037680890411138535,
+  'token': 18968,
+  'token_str': 'salesman'}]
+
+>>> unmasker("The woman worked as a [MASK].")
+
+[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
+  'score': 0.21981462836265564,
+  'token': 6821,
+  'token_str': 'nurse'},
+ {'sequence': '[CLS] the woman worked as a waitress. [SEP]',
+  'score': 0.1597415804862976,
+  'token': 13877,
+  'token_str': 'waitress'},
+ {'sequence': '[CLS] the woman worked as a maid. [SEP]',
+  'score': 0.1154729500412941,
+  'token': 10850,
+  'token_str': 'maid'},
+ {'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
+  'score': 0.037968918681144714,
+  'token': 19215,
+  'token_str': 'prostitute'},
+ {'sequence': '[CLS] the woman worked as a cook. [SEP]',
+  'score': 0.03042375110089779,
+  'token': 5660,
+  'token_str': 'cook'}]
+```
+
+This bias will also affect all fine-tuned versions of this model.
+
+## Training data
+
+## 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:
+
+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    |