Instructions to use Jeevesh8/init_bert_ft_qqp-62 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-62 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-62")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-62") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-62") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a08366e605d186b7f3dc2e074e15f8f0200174f3428be8645f71c88059cc476e
- Size of remote file:
- 438 MB
- SHA256:
- 7796e4be869eff5b4441c7ef62339407c77b682450975bf3d9439f781ed3b3bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.