使用Python构建基于序列标注的上下文纠错系统

import torch
import torch.nn as nn

class BiLSTM_CRF(nn.Module):
    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
        super(BiLSTM_CRF, self).__init__()
        self.embedding_dim = embedding_dim
        self.hidden_dim = hidden_dim
        self.vocab_size = vocab_size
        self.tag_to_ix = tag_to_ix
        self.tagset_size = len(tag_to_ix)

        self.word_embeds = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
                            num_layers=1, bidirectional=True, batch_first=True)
        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)
        self.crf = CRF(self.tagset_size)  # 需自定义CRF层或使用第三方库

    def forward(self, sentences):
        embeds = self.word_embeds(sentences)
        lstm_out, _ = self.lstm(embeds)
        lstm_feats = self.hidden2tag(lstm_out)
        return self.crf.decode(lstm_feats)  # 输出预测标签序列

from transformers import BertTokenizer, BertForTokenClassification
import torch

tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
model = BertForTokenClassification.from_pretrained('bert-base-chinese', num_labels=4)  # 假设4类标签

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)
    return predictions[0].tolist()  # 返回标签序列

text = "他再家编程"
labels = predict(text)  # 输出如 [0, 1, 0, 0, 0]（0=正确，1=替换）

import random
from pypinyin import pinyin, Style

def augment_text(text, p=0.1):
    chars = list(text)
    for i in range(len(chars)):
        if random.random() < p:
            # 同音字替换（简化版）
            py = pinyin(chars[i], style=Style.NORMAL)[0][0]
            candidates = [c for c in ["在", "再", "载"] if pinyin(c)[0][0] == py]
            if candidates:
                chars[i] = random.choice(candidates)
    return ''.join(chars)

text = "他再家编程"
augmented = augment_text(text)  # 可能输出 "他在家编程"

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/correct', methods=['POST'])
def correct_text():
    data = request.json
    text = data.get('text', '')
    labels = predict(text)  # 调用前述预测函数
    corrected = []
    for i, (char, label) in enumerate(zip(text, labels)):
        if label != 0:  # 假设0=正确
            # 简单示例：直接替换为常见正确词（实际需结合候选生成）
            if char == "再" and i > 0 and text[i-1] == "他":
                corrected.append("在")
            else:
                corrected.append(char)
        else:
            corrected.append(char)
    return jsonify({'original': text, 'corrected': ''.join(corrected)})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)