Instructions to use certainstar/Trained-Mul-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use certainstar/Trained-Mul-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="certainstar/Trained-Mul-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("certainstar/Trained-Mul-classification") model = AutoModelForSequenceClassification.from_pretrained("certainstar/Trained-Mul-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
datasets:
- Hello-SimpleAI/HC3-Chinese
- Hello-SimpleAI/HC3
language:
- en
- zh
metrics:
- accuracy
- 本模型采取
HC3的英文数据集和中文对bert-base-multilingual-cased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。