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--- |
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language: |
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- en |
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datasets: |
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- SNLI |
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- MNLI |
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tags: |
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- zero-shot-classification |
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--- |
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A cross-attention NLI model trained for zero-shot and few-shot text classification. |
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The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base), trained with the code from [here](https://github.com/facebookresearch/anli); |
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on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/). |
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Usage: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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import numpy as np |
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model = AutoModelForSequenceClassification.from_pretrained("symanto/mpnet-base-snli-mnli") |
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tokenizer = AutoTokenizer.from_pretrained("symanto/mpnet-base-snli-mnli") |
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input_pairs = [("I like this pizza.", "The sentence is positive."), ("I like this pizza.", "The sentence is negative.")] |
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inputs = tokenizer(["</s></s>".join(input_pair) for input_pair in input_pairs], return_tensors="pt") |
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logits = model(**inputs).logits |
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probs = torch.softmax(logits, dim=1).tolist() |
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print("probs", probs) |
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np.testing.assert_almost_equal(probs, [[0.86, 0.14, 0.00], [0.16, 0.15, 0.69]], decimal=2) |
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``` |
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