Text Classification
Transformers
Safetensors
English
decision-model
typed-decisions
calibration
opendecide
Instructions to use mvbalaji/od1-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mvbalaji/od1-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mvbalaji/od1-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mvbalaji/od1-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download serving.json from mvbalaji/od1-base: direct link, hf CLI and curl.
- Browser
- Download file 156 Bytes
-
https://huggingface.co/mvbalaji/od1-base/resolve/main/serving.json
- Command line
-
hf download hf://mvbalaji/od1-base/serving.json
-
curl -L -o serving.json https://huggingface.co/mvbalaji/od1-base/resolve/main/serving.json
156 Bytes
| { | |
| "na_threshold": 0.9, | |
| "exit_tau": 0.6, | |
| "exit_layer": 16, | |
| "fitted_on": [ | |
| "dev sample", | |
| "OOD validation set" | |
| ], | |
| "never_on": "test sets" | |
| } |