Create inference.py
Browse files- inference.py +26 -0
inference.py
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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import torch
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MODEL_NAME = "ss108/legal-citation-bert"
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tokenizer = AutoTokenizer.from_pretrained("ss108/legal-citation-bert")
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model = AutoModelForTokenClassification.from_pretrained("ss108/legal-citation-bert")
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model.eval()
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def predict(text):
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# Tokenize the input text
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inputs = tokenizer(text, return_tensors="pt", padding=True)
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# Get model predictions
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with torch.no_grad():
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outputs = model(**inputs)
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logits = outputs.logits
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predictions = torch.argmax(logits, dim=-1)
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# Convert predictions to labels
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labels = [model.config.id2label[pred.item()] for pred in predictions[0]]
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# Align labels with tokens
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tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
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result = [{'token': token, 'label': label} for token, label in zip(tokens, labels) if token not in tokenizer.all_special_tokens]
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return result
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