Instructions to use Jeevesh8/init_bert_ft_qqp-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-64")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-64") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-64") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52b52f46f38e1e6b437d873515bcc32b7ccdacb56c4a64be646952d2042eb916
- Size of remote file:
- 438 MB
- SHA256:
- b77841da4775aa88e0a1bee9f8381546d8a58e90bbd3eea2e36119526aa48766
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.