dataset_info:
features:
- name: prompt
dtype: string - name: type
dtype: string - name: qwq32b-classification
dtype: string - name: r1-classification
dtype: string - name: llama70-classification
dtype: string - name: llama-cot-classification
dtype: string
splits: - name: train
num_bytes: 415674
num_examples: 100
download_size: 222444
dataset_size: 415674
configs: - config_name: default
data_files:- split: train
path: data/train-*
- split: train
Eval models for classification on your own data
This dataset contains the results of evaluating reasoning models for classification. It contains the pipeline and the code to run it. You can tune the config to run different prompts over your HF datasets.
Results
| Model | Accuracy | Total | Correct | Empty |
|---|---|---|---|---|
| qwq32b-classification | 92.00% | 100 | 92 | 1 |
| r1-classification | 91.00% | 100 | 91 | 2 |
| llama70-classification | 77.00% | 100 | 77 | 10 |
How to run it
The pipeline uses Inference Providers (Sambanova)
Get the code here and run:
pip install -U huggingface-hub pyyaml
export HF_TOKEN="hf_******"
python dg.py
If everything went well, you should see this:
