.

demo.py

@@ -0,0 +1,18 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_card = "EvgeniaKomleva/rpt"   # you can try other model_card listed in the table above
+tokenizer = AutoTokenizer.from_pretrained(model_card)
+model = AutoModelForSequenceClassification.from_pretrained(model_card)
+
+def score(cxt, hyp):
+  model_input = tokenizer.encode(cxt + "<|endoftext|>" + hyp, return_tensors="pt")
+  result = model(model_input, return_dict=True)
+  return torch.sigmoid(result.logits)
+
+cxt = "I love NLP!"
+hyp_A = "Me too!"
+hyp_B = "Here’s a free textbook (URL) in case anyone needs it."
+
+print('%.3f %s'%(score(cxt, hyp_A).squeeze(), hyp_A))
+print('%.3f %s'%(score(cxt, hyp_B).squeeze(), hyp_B))

special_tokens_map.json

@@ -0,0 +1 @@
+{"pad_token": "<|endoftext|>"}