Merge branch 'main' of https://huggingface.co/symanto/mpnet-base-snli-mnli into main

README.md

@@ -1,3 +1,12 @@
+---
+language: 
+  - en
+datasets:
+- SNLI
+- MNLI
+---
+
+
 This is a small cross attention entailment model trained for zero-shot and few-shot text classification experiments.
 
 The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base) and it has been trained with the code from [here](https://github.com/facebookresearch/anli).
@@ -14,9 +23,10 @@ import numpy as np
 model = AutoModelForSequenceClassification.from_pretrained("symanto/mpnet-base-snli-mnli")
 tokenizer = AutoTokenizer.from_pretrained("symanto/mpnet-base-snli-mnli")
 
-inputs = tokenizer(["I like this pizza. [SEP] The sentence is positive.",  "I like this pizza. [SEP] The sentence is negative."], return_tensors="pt")
+input_pairs = [("I like this pizza.", "The sentence is positive."), ("I like this pizza.", "The sentence is negative.")]
+inputs = tokenizer(["</s></s>".join(input_pair) for input_pair in input_pairs], return_tensors="pt")
 logits = model(**inputs).logits
 probs =  torch.softmax(logits, dim=1).tolist()
 print("probs", probs)
-np.testing.assert_almost_equal(probs, [[0.66, 0.33, 0.01], [0.08, 0.22, 0.70]], decimal=2)
+np.testing.assert_almost_equal(probs, [[0.86, 0.14, 0.00], [0.16, 0.15, 0.69]], decimal=2)
 ```