Instructions to use rsaketh02/quora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rsaketh02/quora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rsaketh02/quora")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rsaketh02/quora") model = AutoModelForSequenceClassification.from_pretrained("rsaketh02/quora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rsaketh02/quora: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/rsaketh02/quora/resolve/main/training_args.bin
- Command line
-
hf download hf://rsaketh02/quora/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rsaketh02/quora/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- a66ccb8d02159215bbd936fdce8258a6118990107af8c4de27571118d65f4996
- Size of remote file:
- 4.54 kB
- SHA256:
- d6a945c3e66258ff6cf708a5363d13e7b3a37433869e713060e168d1b682dce6
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