Feature Extraction
Transformers
Safetensors
autojev_decision
jev
decision
autojev
typed-decision
classification
distillation
custom_code
Instructions to use inclusionAI/LemonDecider with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LemonDecider with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="inclusionAI/LemonDecider", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inclusionAI/LemonDecider", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sft_receipt.json from inclusionAI/LemonDecider: direct link, hf CLI and curl.
- Browser
- Download file 651 Bytes
-
https://huggingface.co/inclusionAI/LemonDecider/resolve/main/sft_receipt.json
- Command line
-
hf download hf://inclusionAI/LemonDecider/sft_receipt.json
-
curl -L -o sft_receipt.json https://huggingface.co/inclusionAI/LemonDecider/resolve/main/sft_receipt.json
651 Bytes
| { | |
| "step": 24, | |
| "dev_accuracy_t1": 0.7735191583633423, | |
| "best_step": 3, | |
| "best_dev_accuracy_t1": 0.7804877758026123, | |
| "lr": 2.2827515498423204e-07, | |
| "examples_seen": 6144, | |
| "temperature_fit": { | |
| "method": "NLL scan on dev 574 under fork _known() semantics, T grid [0.30,5.00]x0.05 (from cached T=1 probs)", | |
| "best_T": 0.6, | |
| "dev_nll_at_T": 0.56265, | |
| "dev_brier_at_T": 0.28261, | |
| "dev_nll_at_1": 0.6016, | |
| "dev_brier_at_1": 0.29885, | |
| "bound": false, | |
| "reason": "best_T=0.60 != serving T=1.0; NOT applied — public-set re-eval required before adoption (dev acc unchanged at T*, argmax-invariant except noul boundary)" | |
| } | |
| } |