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--- |
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license: mit |
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language: |
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- en |
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datasets: climatebert/distilroberta-base-climate-f |
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tags: |
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- fact-checking |
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- climate |
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--- |
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This model fine-tuned ClimateBert on the textual entailment task. Given (claim, evidence) pairs, the model predicts support (entailment), refute (contradict), or not enough info (neutral). |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model = AutoModelForSequenceClassification.from_pretrained('lighteternal/nli-xlm-r-greek') |
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tokenizer = AutoTokenizer.from_pretrained('lighteternal/nli-xlm-r-greek') |
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features = tokenizer(['Δύο άνθρωποι συναντιούνται στο δρόμο', 'Ο δρόμος έχει κόσμο'], |
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['Ένα μαύρο αυτοκίνητο ξεκινάει στη μέση του πλήθους.', 'Ένας άντρας οδηγάει σε ένα μοναχικό δρόμο.'], |
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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)` |
