Instructions to use Jeevesh8/roberta_base_qqp_ft_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_2") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/roberta_base_qqp_ft_2: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Jeevesh8/roberta_base_qqp_ft_2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/roberta_base_qqp_ft_2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/roberta_base_qqp_ft_2/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- dae01dda78807106eaa98917a3525b4f4010b16f9f84e492f1d9ef152d509ed7
- Size of remote file:
- 499 MB
- SHA256:
- 2aa91da754887c3811a6b501a3fd05bbc28cb685b59dcd11a1cb8dfede8ffae0
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