中文词语分类

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：

"1": "人文科学",
"2": "农林渔畜",
"3": "医学",
"4": "城市信息大全",
"5": "娱乐",
"6": "工程与应用科学",
"7": "生活",
"8": "电子游戏",
"9": "社会科学",
"10": "自然科学",
"11": "艺术",
"12": "运动休闲"

类别来源于搜狗词汇的类型

使用样例

import torch
from transformers import AutoTokenizer, BertForSequenceClassification

model_path = "iioSnail/bert-base-chinese-word-classifier"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path)

words = ["2型糖尿病", "太古里", "跑跑卡丁车", "河豚"]
inputs = tokenizer(words, return_tensors='pt', padding=True)
outputs = model(**inputs).logits
outputs = outputs.sigmoid()
preds = outputs > 0.5
for i, pred in enumerate(preds):
    pred = torch.argwhere(pred).view(-1)
    labels = [model.config.id2label[int(id)] for id in pred]
    print(words[i], ":", labels)

输出：

2型糖尿病 : ['医学']
太古里 : ['城市信息大全']
跑跑卡丁车 : ['电子游戏']
河豚 : ['人文科学', '娱乐', '电子游戏', '自然科学']