Instructions to use Jeevesh8/init_bert_ft_qqp-52 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-52 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-52")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-52") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-52") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4004aa90f8f42c7366a865dbea442039bf57648c82a384dbbc7fed61d3ef8aaf
- Size of remote file:
- 438 MB
- SHA256:
- 2c56a122af62868593a08e4d2ce7a271f8c60c62315903a5a978beb86fdda6fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.