Instructions to use Jeevesh8/bert_ft_qqp-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-3") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7af9ea48391e62147c54a801a9466c40729d05f8ff81a8119e95c09eaac9b37
- Size of remote file:
- 438 MB
- SHA256:
- 79724e363f31ae5d19977036f5a40764775d46d61a443d3737afe88a42196ff1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.