Instructions to use avinasht/finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avinasht/finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="avinasht/finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("avinasht/finetuned") model = AutoModelForSequenceClassification.from_pretrained("avinasht/finetuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c64741c9da04a3a7d1cb533236887c85df965a43b04a972a624402fa5e481c0c
- Size of remote file:
- 499 MB
- SHA256:
- 06edecbde36c85bbc12837ab65bee1d98250d444400887db02f150bdfe7b2804
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.