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README.md
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licenses:
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- cc-by-nc-sa
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---
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licenses:
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- cc-by-nc-sa
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---
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Mutual implication score: a symmetric measure of text semantic similarity
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based on a RoBERTA model pretrained for natural language inference
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and fine-tuned for paraphrase detection.
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The following snippet illustrates code usage:
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```python
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from mutual_implication_score import MIS
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mis = MIS(device='cpu')
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source_texts = ['I want to leave this room',
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'Hello world, my name is Nick']
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paraphrases = ['I want to go out of this room',
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'Hello world, my surname is Petrov']
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scores = mis.compute(source_texts, paraphrases)
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print(scores)
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# expected output: [0.9748, 0.0545]
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```
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The first two texts are semantically equivalent, their MIS is close to 1.
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The two other texts have different meanings, and their score is low.
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