Instructions to use Jeevesh8/bert_ft_qqp-29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-29 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-29")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-29") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-29") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6238232c844f07a2e2d112928b10c9aec3eaee77a6e082b23384f47840e053a
- Size of remote file:
- 438 MB
- SHA256:
- 92baf965292e2fbff3f24740934b7d24d885c52bb23037bdf63877cde8db0c68
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.