Instructions to use Jeevesh8/bert_ft_qqp-89 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-89 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-89")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-89") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-89") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49d343d112e6a3ab8f2b70e2c7321244f1c7b047195ad5547ab9baa9d7b65155
- Size of remote file:
- 438 MB
- SHA256:
- 070464c3c5ceeb616e81d4e27c63c7bddf037478ed1b83e99d5232244d1aeb24
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.