Instructions to use Abhibeats95/question_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhibeats95/question_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhibeats95/question_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhibeats95/question_classification") model = AutoModelForSequenceClassification.from_pretrained("Abhibeats95/question_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Abhibeats95/question_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/Abhibeats95/question_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://Abhibeats95/question_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Abhibeats95/question_classification/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- d14d90fe7c30264595fe4ee1732efa0d0b31a8d60ab52914bf1315b79bedd379
- Size of remote file:
- 4.6 kB
- SHA256:
- cc7f62bb1346709b83c35739bc14f8c234adedac9ccfed1fd56837f59bb80bc4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.