Instructions to use Jeevesh8/init_bert_ft_qqp-76 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-76 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-76")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-76") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-76") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 821c83f720ca2f7cef2a88e2025642c7a04293813d9891f7ceb2443d67fa6e81
- Size of remote file:
- 438 MB
- SHA256:
- 092dc8c5e9a6b9ec5d2cece81258a97b01f767d5120ed418a0a8bb91bd9a7436
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