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
- Xet hash:
- 10350bcabe61bc90cdd4b1d1b043c0eafca67e51a3cbb0871423f58d47faca79
- Size of remote file:
- 711 MB
- SHA256:
- aa26f51f5c8da79bac14946824e177b478f8c0efd679a7004cdc4b7ec10ad85b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.