Instructions to use Jeevesh8/bert_ft_qqp-86 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-86 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-86")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-86") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-86") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38cd49d065d7abac3b5ce6bff3abc1ed0c47b75117f4b19f3ed317db233305a5
- Size of remote file:
- 438 MB
- SHA256:
- e7fa7da5b94b9312f04c514daa98abbed53a8cab2d37b94ae2654a0fb9ddb8ff
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