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# -*- coding: utf-8 -*-
"""
@author:XuMing([email protected])
@description:
"""
import gradio as gr
import operator
import torch
from transformers import BertTokenizer, BertForMaskedLM
tokenizer = BertTokenizer.from_pretrained("shibing624/macbert4csc-base-chinese")
model = BertForMaskedLM.from_pretrained("shibing624/macbert4csc-base-chinese")
def ai_text(text):
with torch.no_grad():
outputs = model(**tokenizer(text, padding=True, return_tensors='pt'))
def get_errors(corrected_text, origin_text):
sub_details = []
for i, ori_char in enumerate(origin_text):
if ori_char in [' ', '“', '”', '‘', '’', '琊', '\n', '…', '—', '擤']:
# add unk word
corrected_text = corrected_text[:i] + ori_char + corrected_text[i:]
continue
if i >= len(corrected_text):
continue
if ori_char != corrected_text[i]:
if ori_char.lower() == corrected_text[i]:
# pass english upper char
corrected_text = corrected_text[:i] + ori_char + corrected_text[i + 1:]
continue
sub_details.append((ori_char, corrected_text[i], i, i + 1))
sub_details = sorted(sub_details, key=operator.itemgetter(2))
return corrected_text, sub_details
_text = tokenizer.decode(torch.argmax(outputs.logits, dim=-1), skip_special_tokens=True).replace(' ', '')
corrected_text = _text[:len(text)]
corrected_text, details = get_errors(corrected_text, text)
print(text, ' => ', corrected_text, details)
return corrected_text, details
if __name__ == '__main__':
print(ai_text('少先队员因该为老人让坐'))
examples = [
['真麻烦你了。希望你们好好的跳无'],
['少先队员因该为老人让坐'],
['机七学习是人工智能领遇最能体现智能的一个分知'],
['今天心情很好'],
['他法语说的很好,的语也不错'],
['他们的吵翻很不错,再说他们做的咖喱鸡也好吃'],
]
output_text = gr.outputs.Textbox()
gr.Interface(ai_text, "textbox", "textbox", title="Chinese Text Correction shibing624/macbert4csc-base-chinese",
description="Copy or input error Chinese text. Submit and the machine will correct text.",
examples=examples).launch()
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