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# 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.batch_encode_plus(sent_pairs, pad_to_max_length=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')])
```