readme: ---
  language: en
  widget:
   - text: "my example goes here in the requested language"
  license: mit
  ---
  
  # WORK IN PROGRESS 
  # bettercallbloom-560m
  
  
  Finetuned bloom-560m model on the PileOfLaw - r/legal_advice
  
  
  
  ## Model description
  
  
  
  ## Intended uses & limitations
  
  
  
  ### How to use
  
  
  
  Here is how to use this model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import GPT2Tokenizer, GPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
  model = GPT2Model.from_pretrained('gpt2')
  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 GPT2Tokenizer, TFGPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
  model = TFGPT2Model.from_pretrained('gpt2')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='tf')
  output = model(encoded_input)
  ```
  
  ### Limitations and bias
  
  The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
  unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
  [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
  
  > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
  > that require the generated text to be true.
  >
  > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
  > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
  > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
  > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
  > levels of caution around use cases that are sensitive to biases around human attributes.
  
  Here's an example of how the model can have biased predictions:
  
  ```python
  >>> from transformers import pipeline, set_seed
  >>> generator = pipeline('text-generation', model='gpt2')
  >>> set_seed(42)
  >>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
  
  [{'generated_text': 'The White man worked as a mannequin for'},
   {'generated_text': 'The White man worked as a maniser of the'},
   {'generated_text': 'The White man worked as a bus conductor by day'},
   {'generated_text': 'The White man worked as a plumber at the'},
   {'generated_text': 'The White man worked as a journalist. He had'}]
  
  >>> set_seed(42)
  >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
  
  [{'generated_text': 'The Black man worked as a man at a restaurant'},
   {'generated_text': 'The Black man worked as a car salesman in a'},
   {'generated_text': 'The Black man worked as a police sergeant at the'},
   {'generated_text': 'The Black man worked as a man-eating monster'},
   {'generated_text': 'The Black man worked as a slave, and was'}]
  ```
  
  This bias will also affect all fine-tuned versions of this model.
  
  ## Training data
  
  The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
  pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
  this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
  40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
  [here](https://github.com/openai/gpt-2/blob/master/domains.txt).
  
  ## Training procedure
  
  ### Preprocessing
  
  The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
  vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
  
  The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
  details of training.
  
  ## Evaluation results
  
  The model achieves the following results without any fine-tuning (zero-shot):
  
  | Dataset  | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB    | enwiki8 | text8  | WikiText103 | 1BW   |
  |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
  | (metric) | (PPL)   | (ACC)   | (ACC)  | (ACC)  | (PPL)     | (PPL)  | (BPB)   | (BPC)  | (PPL)       | (PPL) |
  |          | 35.13   | 45.99   | 87.65  | 83.4   | 29.41     | 65.85  | 1.16    | 1,17   | 37.50       | 75.20 |
  
  
  
  