Update README.md

README.md

@@ -30,80 +30,6 @@ learn = cnn_learner(dls, resnet34, metrics=error_rate)
 learn.fine_tune(2)
 ```
 
-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 model to further fine-tune tasks that might be related to classifying animals; however, note that this model is primarily intended to illustrate the ease of integrating fastai-trained models into the HuggingFace Hub. For pretrained image classification models, see the [HuggingFace Hub](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads) and from the task menu select Image Classification.
-
-### 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)
-```
 
 ## Training data