Text Classification
Transformers
Safetensors
English
nli
cross-encoder
qwen3.5
reranker
image-text-to-text
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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlexWortega/openjev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
23 days ago
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Add FinCalc-NLI eval (financial arithmetic) + results for all checkpoints
Verified signatureHi 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](https://huggingface.co/datasets/anespo28/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](https://huggingfa…b639183