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README.md
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license: afl-3.0
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---
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---
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license: afl-3.0
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language:
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- zh
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---
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# 中文词语分类
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本模型对中文词语进行分类(多标签)。对于一个中文词语,其会被分为一个或多个类别,类别有如下:
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```
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"1": "人文科学",
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"2": "农林渔畜",
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"3": "医学",
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"4": "城市信息大全",
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"5": "娱乐",
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"6": "工程与应用科学",
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"7": "生活",
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"8": "电子游戏",
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"9": "社会科学",
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"10": "自然科学",
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"11": "艺术",
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"12": "运动休闲"
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```
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> 类别来源于[搜狗词汇的类型](https://pinyin.sogou.com/dict/cate/index/167)
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# 使用样例
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```python
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import torch
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from transformers import AutoTokenizer, BertForSequenceClassification
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model_path = "iioSnail/bert-base-chinese-word-classifier"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = BertForSequenceClassification.from_pretrained(model_path)
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words = ["2型糖尿病", "太古里", "跑跑卡丁车", "河豚"]
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inputs = tokenizer(words, return_tensors='pt', padding=True)
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outputs = model(**inputs).logits
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outputs = outputs.sigmoid()
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preds = outputs > 0.5
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for i, pred in enumerate(preds):
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pred = torch.argwhere(pred).view(-1)
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labels = [model.config.id2label[int(id)] for id in pred]
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print(words[i], ":", labels)
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```
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输出:
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```
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2型糖尿病 : ['医学']
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太古里 : ['城市信息大全']
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跑跑卡丁车 : ['电子游戏']
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河豚 : ['人文科学', '娱乐', '电子游戏', '自然科学']
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```
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