Instructions to use Jeevesh8/bert_ft_qqp-91 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-91 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-91")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-91") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-91") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2c219b4b0565f50f308c0aaa2fadbb97a4c0ca11b1d3e5ec0c70776e59b70c0
- Size of remote file:
- 438 MB
- SHA256:
- 8b619c09bb637236340f105e8f48b70901e9f146a82c0093b91a0a9d963cdd88
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