Instructions to use Jeevesh8/init_bert_ft_qqp-49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-49")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-49") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-49", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jeevesh8/init_bert_ft_qqp-49: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Jeevesh8/init_bert_ft_qqp-49/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jeevesh8/init_bert_ft_qqp-49/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jeevesh8/init_bert_ft_qqp-49/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0829c8dc3ac8888bceaefa566aefb0841d5b26d19aa1144fcfb3fe08214f1488
- Size of remote file:
- 438 MB
- SHA256:
- d45f581ec81de68e14d588be937cb47a7f99a7b7e7d4f01d530db1c551fc2cb7
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