readme: ---
  language:
  - ar
  - bg
  - de
  - el
  - en
  - es
  - fr
  - ru
  - th
  - tr
  - ur
  - vn
  - zh
  - multilingual
  tags:
  - zero-shot-classification
  datasets:
  - SNLI
  - MNLI
  - ANLI
  - XNLI
  ---
  
  
  A cross-attention NLI model trained for zero-shot and few-shot text classification.
  
  The base model is [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), trained with the code from [here](https://github.com/facebookresearch/anli);
  on [SNLI](https://nlp.stanford.edu/projects/snli/), [MNLI](https://cims.nyu.edu/~sbowman/multinli/), [ANLI](https://github.com/facebookresearch/anli) and [XNLI](https://github.com/facebookresearch/XNLI).
  
  Usage:
  
  ```python
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  import torch
  import numpy as np
  
  model = AutoModelForSequenceClassification.from_pretrained("symanto/xlm-roberta-base-snli-mnli-anli-xnli")
  tokenizer = AutoTokenizer.from_pretrained("symanto/xlm-roberta-base-snli-mnli-anli-xnli")
  
  input_pairs = [
                 ("I like this pizza.", "The sentence is positive."),
                 ("I like this pizza.", "The sentence is negative."),
                 ("I mag diese Pizza.", "Der Satz ist positiv."),
                 ("I mag diese Pizza.", "Der Satz ist negativ."),
                 ("Me gusta esta pizza.", "Esta frase es positivo."),
                 ("Me gusta esta pizza.", "Esta frase es negativo."),
  ]
  inputs = tokenizer(input_pairs, truncation="only_first", return_tensors="pt", padding=True)
  logits = model(**inputs).logits
  probs = torch.softmax(logits, dim=1)
  probs = probs[..., [0]].tolist()
  print("probs", probs)
  np.testing.assert_almost_equal(probs, [[0.83], [0.04], [1.00], [0.00], [1.00], [0.00]], decimal=2)
  ```
  