Instructions to use Jeevesh8/roberta_base_qqp_ft_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_13")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_13") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/roberta_base_qqp_ft_13: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Jeevesh8/roberta_base_qqp_ft_13/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/roberta_base_qqp_ft_13/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/roberta_base_qqp_ft_13/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- b288a6edb37e30e3edc9b4d681d57750313a54d4283813200bb5fa2df61e8f70
- Size of remote file:
- 499 MB
- SHA256:
- 79a16b394ca145dd885f7848f128b32e65aabd9b47369e7736386ce575cf2325
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