Text Classification
Transformers
Safetensors
decision-model
classification
julia
open-jev
head-finetune
low-resource
Instructions to use SHSLab/Qyvos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHSLab/Qyvos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SHSLab/Qyvos")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SHSLab/Qyvos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qyvos v1: Julia-1 backbone (bit-exact) + Open-Jev head fine-tune (30k rows, low-RAM protocol)
31f7037 verified Download label_map.json from SHSLab/Qyvos: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/SHSLab/Qyvos/resolve/main/label_map.json
- Command line
-
hf download hf://SHSLab/Qyvos/label_map.json
-
curl -L -o label_map.json https://huggingface.co/SHSLab/Qyvos/resolve/main/label_map.json
125 Bytes
| { | |
| "0": "choice", | |
| "1": "score", | |
| "2": "noul", | |
| "note": "qtype id -> Open-Jev kind; options are scored in given order" | |
| } | |