Update README.md

README.md

@@ -5,4 +5,28 @@ datasets:
 language:
 - en
 pipeline_tag: sentence-similarity
----
+---
+## Model to score relative persuasive language between pairs
+More info about training, evaluation, and use in paper is here: link.
+
+Python:
+```python 
+from transformers import AutoModelForSequenceClassification,AutoTokenizer
+import torch
+modelname='APauli/Persuasive_language_in_pairs'
+model = AutoModelForSequenceClassification.from_pretrained(modelname)
+tokenizer = AutoTokenizer.from_pretrained(modelname)
+
+def predict(textA, textB, model,tokenizer):
+    encoded_input = tokenizer(textA, textB, padding=True, truncation=True,max_length=256, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**encoded_input).logits
+    score1=logits.detach().cpu().numpy()
+    #flipped
+    encoded_input = tokenizer(textB, textA, padding=True, truncation=True,max_length=256, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**encoded_input).logits
+    score2=logits.detach().cpu().numpy()*(-1)
+    score = (score1+score2)/2
+    return score
+```