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_4/checkpoint-504/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_4/checkpoint-504/optimizer.pt
- Command line
-
hf download hf://a-01a/QSolver_Encoder_V2/fold_4/checkpoint-504/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/a-01a/QSolver_Encoder_V2/resolve/main/fold_4/checkpoint-504/optimizer.pt
1.36 GB
- Xet hash:
- c406b68ba1a19cb234f3c4ed607470346f40b2c1ae8b18eb179d3a5f6ccc619c
- Size of remote file:
- 1.36 GB
- SHA256:
- d9348353ba127eecf2a2c6dc822d2671d4f5417404b2534ac47266f07c90e7dc
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