readme: ---
  language: 
    - en
  datasets:
  - SNLI
  - MNLI
  tags:
  - zero-shot-classification
  ---
  
  
  A cross-attention NLI model trained for zero-shot and few-shot text classification.
  
  The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base), trained with the code from [here](https://github.com/facebookresearch/anli);
  on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
  
  Usage:
  
  ```python
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  import torch
  import numpy as np
  
  model = AutoModelForSequenceClassification.from_pretrained("symanto/mpnet-base-snli-mnli")
  tokenizer = AutoTokenizer.from_pretrained("symanto/mpnet-base-snli-mnli")
  
  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, [[0.86, 0.14, 0.00], [0.16, 0.15, 0.69]], decimal=2)
  ```
  