Instructions to use Jeevesh8/roberta_base_qqp_ft_21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_21")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_21") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/roberta_base_qqp_ft_21: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Jeevesh8/roberta_base_qqp_ft_21/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/roberta_base_qqp_ft_21/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/roberta_base_qqp_ft_21/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 12d89117ac57b8a4a8075ae228ca0a1c45848ad29e99240214bf74da98c30d29
- Size of remote file:
- 499 MB
- SHA256:
- cef8d6bd5d6ba35d1088665a1c25ea49598bdf88d8b709c26f8fd0c8c48e6d72
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