写一个中文分词模型程序,使用的预训练模型为pkuseg,微调的数据是预先用换行符分隔词语的文本,用向量机模型微调
时间: 2023-06-25 09:05:06 浏览: 66
抱歉,作为AI语言模型,我无法在本地环境上运行代码。但我可以提供一个大致的实现流程:
1. 安装 pkuseg 和 scikit-learn 库
2. 加载 pkuseg 预训练模型
```python
import pkuseg
seg = pkuseg.pkuseg()
```
3. 加载微调数据
```python
with open('train.txt', 'r', encoding='utf-8') as f:
train = f.readlines()
```
4. 对微调数据进行分词处理
```python
train_seg = []
for line in train:
seg_list = seg.cut(line.strip())
train_seg.append(' '.join(seg_list))
```
5. 加载标签数据
```python
with open('label.txt', 'r', encoding='utf-8') as f:
label = f.readlines()
```
6. 将标签数据转换为数字标签
```python
label_dict = {}
num_labels = 0
for line in label:
if line.strip() not in label_dict:
label_dict[line.strip()] = num_labels
num_labels += 1
y_train = [label_dict[line.strip()] for line in label]
```
7. 使用 TfidfVectorizer 将文本数据转换为向量表示
```python
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(train_seg)
```
8. 使用 SVM 进行分类
```python
from sklearn.svm import SVC
svm = SVC()
svm.fit(X_train, y_train)
```
9. 保存模型
```python
import joblib
joblib.dump(svm, 'segmentation_model.pkl')
```
10. 加载模型并使用
```python
clf = joblib.load('segmentation_model.pkl')
text = '我爱北京天安门'
text_seg = seg.cut(text)
text_seg_str = ' '.join(text_seg)
text_vec = vectorizer.transform([text_seg_str])
label_num = clf.predict(text_vec)[0]
label_str = list(label_dict.keys())[list(label_dict.values()).index(label_num)]
print(f'分词结果为:{text_seg}')
print(f'分类结果为:{label_str}')
```