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:
- f5cf88dec6adb769898762312c7a05c8ef81d4ebfa7c567305912d4288472e50
- Size of remote file:
- 1.06 kB
- SHA256:
- 76320ce4ec97c5c9aba00c5f9ac079ba0900242917453092d9390b52938f00f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.