Instructions to use shibing624/bart4csc-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shibing624/bart4csc-base-chinese with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shibing624/bart4csc-base-chinese")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("shibing624/bart4csc-base-chinese")
model = AutoModelForSeq2SeqLM.from_pretrained("shibing624/bart4csc-base-chinese")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shibing624/bart4csc-base-chinese with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shibing624/bart4csc-base-chinese"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shibing624/bart4csc-base-chinese",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shibing624/bart4csc-base-chinese

SGLang

How to use shibing624/bart4csc-base-chinese with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "shibing624/bart4csc-base-chinese" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shibing624/bart4csc-base-chinese",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "shibing624/bart4csc-base-chinese" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shibing624/bart4csc-base-chinese",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shibing624/bart4csc-base-chinese with Docker Model Runner:
```
docker model run hf.co/shibing624/bart4csc-base-chinese
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Bart for Chinese Spelling Correction(bart4csc) Model

BART中文拼写纠错模型

bart4csc-base-chinese evaluate SIGHAN2015 test data：

Sentence Level: acc:0.6845, precision:0.6984, recall:0.6354, f1:0.6654

case:

input_text	pred
辰导中引述她的话说：核子间题的解决之道系于克什米尔纷争。	报导中引述她的话说：核子问题的解决之道系于克什米尔纷争。
报导并末说明事故发生的原因。	报导并未说明事故发生的原因。

训练使用了SIGHAN+Wang271K中文纠错数据集，在SIGHAN2015的测试集上达到接近SOTA水平。

Usage

本项目开源在文本生成项目：textgen，可支持Bart模型，通过如下命令调用：

Install package:

pip install -U textgen

from transformers import BertTokenizerFast
from textgen import BartSeq2SeqModel

tokenizer = BertTokenizerFast.from_pretrained('shibing624/bart4csc-base-chinese')
model = BartSeq2SeqModel(
    encoder_type='bart',
    encoder_decoder_type='bart',
    encoder_decoder_name='shibing624/bart4csc-base-chinese',
    tokenizer=tokenizer,
    args={"max_length": 128, "eval_batch_size": 128})
sentences = ["少先队员因该为老人让坐"]
print(model.predict(sentences))
# ['少先队员应该为老人让座']

模型文件组成：

bart4csc-base-chinese
    ├── config.json
    ├── model_args.json
    ├── pytorch_model.bin
    ├── special_tokens_map.json
    ├── tokenizer_config.json
    ├── spiece.model
    └── vocab.txt

训练数据集

SIGHAN+Wang271K中文纠错数据集

数据集	语料	下载链接	压缩包大小
`SIGHAN+Wang271K中文纠错数据集`	SIGHAN+Wang271K(27万条)	百度网盘（密码01b9）	106M
`原始SIGHAN数据集`	SIGHAN13 14 15	官方csc.html	339K
`原始Wang271K数据集`	Wang271K	Automatic-Corpus-Generation dimmywang提供	93M

SIGHAN+Wang271K中文纠错数据集，数据格式：

[
    {
        "id": "B2-4029-3",
        "original_text": "晚间会听到嗓音，白天的时候大家都不会太在意，但是在睡觉的时候这嗓音成为大家的恶梦。",
        "wrong_ids": [
            5,
            31
        ],
        "correct_text": "晚间会听到噪音，白天的时候大家都不会太在意，但是在睡觉的时候这噪音成为大家的恶梦。"
    },
]

如果需要训练Bart模型，请参考https://github.com/shibing624/textgen/blob/main/examples/seq2seq/training_bartseq2seq_zh_demo.py
了解更多纠错模型，请移步：https://github.com/shibing624/pycorrector

Citation

@software{textgen,
  author = {Xu Ming},
  title = {textgen: Implementation of Text Generation models},
  year = {2022},
  url = {https://github.com/shibing624/textgen},
}

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Safetensors

Model size

0.1B params

Tensor type

F32

shibing624
/

bart4csc-base-chinese

Bart for Chinese Spelling Correction(bart4csc) Model

Usage

训练数据集

SIGHAN+Wang271K中文纠错数据集

Citation

Dataset used to train shibing624/bart4csc-base-chinese

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