Instructions to use Shobhank-iiitdwd/Distilroberta-base-QQP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shobhank-iiitdwd/Distilroberta-base-QQP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shobhank-iiitdwd/Distilroberta-base-QQP")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shobhank-iiitdwd/Distilroberta-base-QQP") model = AutoModelForSequenceClassification.from_pretrained("Shobhank-iiitdwd/Distilroberta-base-QQP") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2e154c1cbeaf4cc14e4f98d5fb98a20ff155b2abcc8b59c8f70366183d42944
- Size of remote file:
- 328 MB
- SHA256:
- 81ee85c07ec9df3edd54b02c4fc70881ee5e1903d0d1ab0b79e3eb4e876cf816
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.