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README.md
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---
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tags:
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- Chinese Medical
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- Punctuation Restoration
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language:
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- zh
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license: mit
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pipeline_tag: token-classification
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base_model: rickltt/pmp-h256
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---
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## Example Usage
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```python
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import torch
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import jieba
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import numpy as np
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from classifier import BertForMaskClassification
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from transformers import AutoTokenizer, AutoConfig, BertForTokenClassification
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label_list = ["O","COMMA","PERIOD","COLON"]
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label2punct = {
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"COMMA": ",",
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"PERIOD": "。",
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"COLON":":",
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}
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model_name_or_path = "pmp-h312"
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
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model = BertForMaskClassification.from_pretrained(model_name_or_path)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def punct(text):
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tokenize_words = jieba.lcut(''.join(text))
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mask_tokens = []
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for word in tokenize_words:
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mask_tokens.extend(word)
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mask_tokens.append("[MASK]")
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tokenized_inputs = tokenizer(mask_tokens,is_split_into_words=True, return_tensors="pt")
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with torch.no_grad():
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logits = model(**tokenized_inputs).logits
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predictions = logits.argmax(-1).tolist()
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predictions = predictions[0]
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tokens = tokenizer.convert_ids_to_tokens(tokenized_inputs["input_ids"][0])
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result =[]
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print(tokens)
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print(predictions)
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for token, prediction in zip(tokens, predictions):
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if token =="[CLS]" or token =="[SEP]":
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continue
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if token == "[MASK]":
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label = label_list[prediction]
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if label != "O":
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punct = label2punct[label]
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result.append(punct)
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else:
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result.append(token)
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return "".join(result)
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text = '肝浊音界正常肝上界位于锁骨中线第五肋间移动浊音阴性肾区无叩痛'
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print(punct(text))
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# 肝浊音界正常,肝上界位于锁骨中线第五肋间,移动浊音阴性,肾区无叩痛。
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```
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# Acknowledgments
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This work was in part supported by Shenzhen Science and Technology Program (No:JCYJ20210324135809025).
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# Citations
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Coming Soon
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# License
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MIT
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