Instructions to use Jeevesh8/init_bert_ft_qqp-67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-67 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-67")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-67") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-67") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e5171431e3b4b4417a25714c06540ab63fcae46797100cac443257c6b79fbc5
- Size of remote file:
- 438 MB
- SHA256:
- 48fc3c3e703404272de41e4e78d17863a6f808984889d880ea9fa44c10e355e3
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