Instructions to use zabor2432/fine_tuned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zabor2432/fine_tuned_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zabor2432/fine_tuned_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zabor2432/fine_tuned_model") model = AutoModelForSequenceClassification.from_pretrained("zabor2432/fine_tuned_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6361bd2ff74d01b9d5b5f9280d7d9822fda30a8a8b549db09e30f094a27f04cc
- Size of remote file:
- 5.2 kB
- SHA256:
- e98be18985011666994063888b3ad866f440242d2c56432adef296e380387291
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.