Instructions to use Jeevesh8/bert_ft_qqp-75 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-75 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-75")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-75") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-75") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 108745f8ff8d3ce92a45b5dce436ea2e7efb9ae365157831c6a55d7226f76ec7
- Size of remote file:
- 438 MB
- SHA256:
- 20671f2a0954fadcdd92d43eb38ca312f224e93915ab33b3d61ce4e47153121c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.