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/checkpoint-378/optimizer.pt from a-01a/QSolver_Encoder_V2: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/a-01a/QSolver_Encoder_V2/resolve/main/fold_2/checkpoint-378/optimizer.pt
- Command line
-
hf download hf://a-01a/QSolver_Encoder_V2/fold_2/checkpoint-378/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/a-01a/QSolver_Encoder_V2/resolve/main/fold_2/checkpoint-378/optimizer.pt
1.36 GB
- Xet hash:
- e7f6982794e5919417f8c02f01e55f862b311b13429c90311de8747665ca62b6
- Size of remote file:
- 1.36 GB
- SHA256:
- 5ea86c2834bcd489c40766050836b1b43ef3d90ba1a23e9777889f10b788f6cd
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