Instructions to use sidd272/Distilbert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sidd272/Distilbert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sidd272/Distilbert_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sidd272/Distilbert_model") model = AutoModelForSequenceClassification.from_pretrained("sidd272/Distilbert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03cd5fd472e47c52b780f640b629b582e38b5a6ea34e7e42e9c321120b844f12
- Size of remote file:
- 268 MB
- SHA256:
- 53fca3e793c51267cb4ae501b2fef4a9b53cb94951bf47682114e4403da2c2e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.