Text Classification
Laya
ONNX
Transformers
Safetensors
PyTorch
English
nirnay
banking77
intent-classification
intent-detection
calibrated-decisions
calibration
decision-model
system-one
small-language-model
edge-ai
on-device
guardrails
triage
lora
rlcd
Eval Results (legacy)
Instructions to use eulogik/nirnay-450m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Laya
How to use eulogik/nirnay-450m with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Transformers
How to use eulogik/nirnay-450m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eulogik/nirnay-450m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("eulogik/nirnay-450m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval/banking77_phase_a.json from eulogik/nirnay-450m: direct link, hf CLI and curl.
- Browser
- Download file 329 Bytes
-
https://huggingface.co/eulogik/nirnay-450m/resolve/main/eval/banking77_phase_a.json
- Command line
-
hf download hf://eulogik/nirnay-450m/eval/banking77_phase_a.json
-
curl -L -o banking77_phase_a.json https://huggingface.co/eulogik/nirnay-450m/resolve/main/eval/banking77_phase_a.json
329 Bytes
| { | |
| "accuracy": 0.8655844155844156, | |
| "brier": 0.2396956351758626, | |
| "checkpoint": "artifacts/phase_a/phase_a.pt", | |
| "data": "data/banking77/test.csv", | |
| "device": "mps", | |
| "ece_fitted": 0.032533240665770846, | |
| "ece_raw": 0.10981167645371878, | |
| "mean_conf": 0.9740734770872643, | |
| "n": 3080, | |
| "temperature": 2.1779661016949152 | |
| } | |