readme: 
  
  # Roberta Large STS-B
  
  This model is a fine tuned RoBERTA model over STS-B.
  It was trained with these params:
  !python /content/transformers/examples/text-classification/run_glue.py \
      --model_type roberta \
      --model_name_or_path roberta-large \
      --task_name STS-B \
      --do_train \
      --do_eval \
      --do_lower_case \
      --data_dir /content/glue_data/STS-B/ \
      --max_seq_length 128 \
      --per_gpu_eval_batch_size=8   \
      --per_gpu_train_batch_size=8   \
      --learning_rate 2e-5 \
      --num_train_epochs 3.0 \
      --output_dir /content/roberta-sts-b
  
  
  ## How to run
  
  ```python
  
  
  
  import toolz
  import torch
  batch_size = 6
  
  def roberta_similarity_batches(to_predict):
    batches = toolz.partition(batch_size, to_predict)
    similarity_scores = []  
    for batch in batches: 
      sentences = [(sentence_similarity["sent1"], sentence_similarity["sent2"])  for sentence_similarity in batch]   
      batch_scores = similarity_roberta(model, tokenizer,sentences)
      similarity_scores = similarity_scores + batch_scores[0].cpu().squeeze(axis=1).tolist()
    return similarity_scores
  
  def similarity_roberta(model, tokenizer, sent_pairs):
    batch_token = tokenizer(sent_pairs, padding='max_length', truncation=True, max_length=500)
    res = model(torch.tensor(batch_token['input_ids']).cuda(), attention_mask=torch.tensor(batch_token["attention_mask"]).cuda())  
    return res
  
  similarity_roberta(model, tokenizer, [('NEW YORK--(BUSINESS WIRE)--Rosen Law Firm, a global investor rights law firm, announces it is investigating potential securities claims on behalf of shareholders of Vale S.A. ( VALE ) resulting from allegations that Vale may have issued materially misleading business information to the investing public',
                                         'EQUITY ALERT: Rosen Law Firm Announces Investigation of Securities Claims Against Vale S.A. – VALE')])
                                         
  ```                                 
  