Instructions to use Jeevesh8/bert_ft_qqp-22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-22")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-22") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-22") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77f8b4bffb15bd6586222aa8bfaf0e15b0f0a64c8b6383631ae2ff5b0087aaf7
- Size of remote file:
- 438 MB
- SHA256:
- 44ea997c3a842357a30b5d40d66dda91a27e73f0b5667c9b2a74f1c3c02f8356
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