Instructions to use certainstar/Trained-Chinese-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use certainstar/Trained-Chinese-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="certainstar/Trained-Chinese-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("certainstar/Trained-Chinese-classification") model = AutoModelForSequenceClassification.from_pretrained("certainstar/Trained-Chinese-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from certainstar/Trained-Chinese-classification: direct link, hf CLI and curl.
- Browser
- Download file 332 Bytes
-
https://huggingface.co/certainstar/Trained-Chinese-classification/resolve/main/README.md
- Command line
-
hf download hf://certainstar/Trained-Chinese-classification/README.md
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curl -L -o README.md https://huggingface.co/certainstar/Trained-Chinese-classification/resolve/main/README.md
332 Bytes
metadata
license: mit
datasets:
- Hello-SimpleAI/HC3-Chinese
metrics:
- accuracy
language:
- zh
- 本模型采取
HC3的中文数据集对bert-base-chinese模型进行三轮训练得到结果。 - 其作用是对文本是否为
GPT生成进行分类,所得Label为0,则不为GPT生成,反之为1,则是。