kopia lustrzana https://github.com/thinkst/zippy
18 wiersze
608 B
Python
18 wiersze
608 B
Python
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#!/usr/bin/env python3
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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tokenizer = AutoTokenizer.from_pretrained("roberta-base-openai-detector")
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model = AutoModelForSequenceClassification.from_pretrained("roberta-base-openai-detector")
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def classify_text(s : str):
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inputs = tokenizer(s, return_tensors='pt')
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with torch.no_grad():
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logits = model(**inputs).logits
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pc = model.config.id2label[logits.argmax().item()]
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conf = max(torch.softmax(logits, dim=1).tolist()[0])
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if pc == 'Real':
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return ('Human', conf)
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return ('AI', conf)
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