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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9745390fdeb5d2a203caa531d505d7c53ba981c398d2d07cd00e82df693726e5
- Size of remote file:
- 1.42 GB
- SHA256:
- afd8766b32c28117ab1e9cb56fff58054d24ba23e768a4a1124fe9cf54ee1ed6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.