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_5/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_5/checkpoint-504/optimizer.pt
- Command line
-
hf download hf://a-01a/QSolver_Encoder_V2/fold_5/checkpoint-504/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/a-01a/QSolver_Encoder_V2/resolve/main/fold_5/checkpoint-504/optimizer.pt
1.36 GB
- Xet hash:
- 5f1c65e0c96dfa91ca0ea4161dfe0ab81206a87e15cc5964a3e8a0269fe92678
- Size of remote file:
- 1.36 GB
- SHA256:
- b5da8e11e28230ab96f2ef0575704a6d261bb96318a408591a19adf0fb8312be
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