Text Classification
Transformers
Safetensors
deberta
deberta-v3
multiple-choice
question-answering
awp
Instructions to use a-01a/QSolver_Encoder_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use a-01a/QSolver_Encoder_V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="a-01a/QSolver_Encoder_V2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("a-01a/QSolver_Encoder_V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download fold_2/training_args.bin from a-01a/QSolver_Encoder_V2: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/a-01a/QSolver_Encoder_V2/resolve/main/fold_2/training_args.bin
- Command line
-
hf download hf://a-01a/QSolver_Encoder_V2/fold_2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/a-01a/QSolver_Encoder_V2/resolve/main/fold_2/training_args.bin
5.27 kB
- Xet hash:
- 7ef0e089b4d21db4d6144671f6cbadd1953498f59a5deddd9520268181c7b262
- Size of remote file:
- 5.27 kB
- SHA256:
- 00e4217a8c9e5660caeb65585c37f36af756ac270a9b711f48a8ee2410c50552
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.