Instructions to use AlexWortega/openjev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexWortega/openjev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlexWortega/openjev")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlexWortega/openjev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add FinCalc-NLI eval (financial arithmetic) + results for all checkpoints
Hi Alex, thanks for openjev. I use it as the first stage of a judge cascade for banking agents, and it's excellent on lookups and faithfulness. I found one gap: claims that need arithmetic (ratios, covenant tests, net figures).
This PR adds code/eval_fincalc.py, in the style of eval_extra.py and built on your NLIScorer, unchanged. It evaluates any checkpoint on FinCalc-NLI (MIT, synthetic, labels computed in code). It also adds one line to "What's inside".
Results on the 4,000-item test split (run on HF Jobs with your code/eval.py as-is):
| checkpoint | FinCalc acc | arithmetic slips caught | wrong-entity figures caught | covenant pass/breach |
|---|---|---|---|---|
| 4B v5 | 63.2% | 30% | 33% | 65% |
| 4B v2 | 58.2% | 34% | 39% | 17% |
| 0.8B v2s-long | 50.5% | 12% | 7% | 32% |
| 4B original | 31.9% | 0% | 0% | 0% |
A LoRA on the original 4B with general-NLI replay reaches 94.4% (openjev-fincalc-4b, demo). If useful, the generator could also join your data mix for a future version. Happy to adapt anything to your conventions.
Tony Esposito