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--- |
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language: ja |
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license: cc-by-4.0 |
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library_name: sentence-transformers |
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tags: |
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- xlm-roberta |
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- nli |
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datasets: |
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- jnli |
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- jsick |
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--- |
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# Japanese Natural Language Inference Model |
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class, [gradient accumulation PR](https://github.com/UKPLab/sentence-transformers/pull/1092), and the code from [CyberAgentAILab/japanese-nli-model](https://github.com/CyberAgentAILab/japanese-nli-model). |
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## Training Data |
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The model was trained on the [JGLUE-JNLI](https://github.com/yahoojapan/JGLUE) and [JSICK](https://github.com/verypluming/JSICK) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick') |
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model = AutoModelForSequenceClassification.from_pretrained('cyberagent/xlm-roberta-large-jnli-jsick') |
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features = tokenizer(["εδΎγθ΅°γ£γ¦γγη«γθ¦γ¦γγ", "η«γθ΅°γ£γ¦γγ"], ["η«γθ΅°γ£γ¦γγ", "εδΎγθ΅°γ£γ¦γγ"], padding=True, truncation=True, return_tensors="pt") |
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model.eval() |
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with torch.no_grad(): |
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scores = model(**features).logits |
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label_mapping = ['contradiction', 'entailment', 'neutral'] |
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labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)] |
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print(labels) |
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``` |
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