# Pseudo-Native-BART-CGEC

This model is a cutting-edge CGEC model based on [Chinese BART-large](https://huggingface.co/fnlp/bart-large-chinese).
It is trained with about 100M pseudo native speaker CGEC training data generated by heuristic rules.
More details can be found in our [Github](https://github.com/HillZhang1999/NaSGEC) and the [paper](https://arxiv.org/pdf/2305.16023.pdf).

## Usage

pip install transformers

```
from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline
tokenizer = BertTokenizer.from_pretrained("HillZhang/pseudo_native_bart_CGEC")
model = BartForConditionalGeneration.from_pretrained("HillZhang/pseudo_native_bart_CGEC")
encoded_input = tokenizer(["北京是中国的都。", "他说：”我最爱的运动是打蓝球“", "我每天大约喝5次水左右。", "今天，我非常开开心。"], return_tensors="pt", padding=True, truncation=True)
if "token_type_ids" in encoded_input:
    del encoded_input["token_type_ids"]
output = model.generate(**encoded_input)
print(tokenizer.batch_decode(output, skip_special_tokens=True))
```

## Citation

```
@inproceedings{zhang-etal-2023-nasgec,
    title = "{Na}{SGEC}: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts",
    author = "Zhang, Yue  and
      Zhang, Bo  and
      Jiang, Haochen  and
      Li, Zhenghua  and
      Li, Chen  and
      Huang, Fei  and
      Zhang, Min"
    booktitle = "Findings of ACL",
    year = "2023"
    }
```
{"language": "zh", "license": "apache-2.0", "tags": ["text2text-generation", "Chinese", "seq2seq", "grammar"]}
transformers pytorch bart text2text-generation Chinese seq2seq grammar zh arxiv:2305.16023 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #Chinese #seq2seq #grammar #zh #arxiv-2305.16023 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

# Pseudo-Native-BART-CGEC

This model is a cutting-edge CGEC model based on Chinese BART-large.
It is trained with about 100M pseudo native speaker CGEC training data generated by heuristic rules.
More details can be found in our Github and the paper.

## Usage

pip install transformers




# Pseudo-Native-BART-CGEC

This model is a cutting-edge CGEC model based on Chinese BART-large.
It is trained with about 100M pseudo native speaker CGEC training data generated by heuristic rules.
More details can be found in our Github and the paper. ## Usage

pip install transformers
TAGS
#transformers #pytorch #bart #text2text-generation #Chinese #seq2seq #grammar #zh #arxiv-2305.16023 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Pseudo-Native-BART-CGEC

This model is a cutting-edge CGEC model based on Chinese BART-large.
It is trained with about 100M pseudo native speaker CGEC training data generated by heuristic rules.
More details can be found in our Github and the paper. ## Usage

pip install transformers