Modelpublic

CodeTed/Chinese_Spelling_Correction_T5

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face

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Model Card

CSC T5 - T5 for Traditional and Simplified Chinese Spelling Correction

This model was obtained by instruction-tuning the corresponding ClueAI/PromptCLUE-base-v1-5 model on the spelling error corpus.

Model Details

Model Description

—Language(s) (NLP): Chinese
—Pretrained from model: ClueAI/PromptCLUE-base-v1-5
—Pretrained by dataset: 1M UDN news corpus
—Finetuned by dataset: shibing624/CSC spelling error corpus (CN + TC)

Model Sources

—Repository: https://github.com/TedYeh/Chinese_spelling_Correction

Evaluation

—Chinese spelling error correction task(SIGHAN2015)：
—FPR: False Positive Rate

Model	Base Model	accuracy	recall	precision	F1	FPR
GECToR	hfl/chinese-macbert-base	71.7	71.6	71.8	71.7	28.2
GECToR_large	hfl/chinese-macbert-large	73.7	76.5	72.5	74.4	29.1
T5 w/ pretrain	ClueAI/PromptCLUE-base-v1-5	79.2	69.2	85.8	76.6	11.1
T5 w/o pretrain	ClueAI/PromptCLUE-base-v1-5	75.1	63.1	82.2	71.4	13.3
PTCSpell		N/A	79.0	89.4	83.8	N/A
MDCSpell		N/A	77.2	81.5	79.3	N/A

Usage

python

from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("CodeTed/Chinese_Spelling_Correction_T5")
model = T5ForConditionalGeneration.from_pretrained("CodeTed/Chinese_Spelling_Correction_T5")
input_text = '糾正句子裡的錯字: 為了降低少子化，政府可以堆動獎勵生育的政策。'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=256)
edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

Related Project

CodeTed/CGEDit - Chinese Grammatical Error Diagnosis by Task-Specific Instruction Tuning