Create README.md (#3917)

model_cards/SparkBeyond/roberta-large-sts-b/README.md

+
+
+# 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/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')])
+                                       
+```