verdict-small

Try it in the browser: https://huggingface.co/spaces/Manav2op/verdict · Colab: https://colab.research.google.com/github/Manavarya09/verdict/blob/main/examples/verdict_quickstart.ipynb

The default encoder of Verdict: small, fast, honest decision models. multilingual-e5-small (118M) fine-tuned on a typed-decision mix of 14 public datasets (intent, NLI, ordinal reviews, safety) so that cosine(input, option) × 20 is a good logit over a question's options. Banking77, SST-5 and ToxicChat were never in the mix; they are the held-out zero-shot numbers below.

pip install verdictml
from verdict import Verdict
v = Verdict()                      # loads this model
v.choose("Billed twice, refund or we cancel", ["billing", "technical", "sales"])
v.check("Can I talk to a person?", claim="the user asks for a human")

Also runs in the browser via transformers.js (onnx/model_quantized.onnx, int8, 118 MB):

const extractor = await pipeline("feature-extraction", "Manav2op/verdict-small", { dtype: "q8" });

Held-out zero-shot (full test sets)

suite	base e5-small	verdict-small
Banking77 (3,076) accuracy	0.594	0.556
SST-5 (2,210) accuracy	0.274	0.403
ToxicChat (5,083) AUROC	0.59	0.892

With 16 labels per class and the package's heads: Banking77 0.842, ToxicChat AUROC 0.939. Every number reproduces with python -m bench.run in the repo; protocol and all rows in docs/BENCHMARKS.md.

Training: python -m train.train (repo), 2,000 steps, batch 32, lr 2e-5, Apple M5. Data mix and caps: train/data.py. Prefixes: query: for inputs, passage: for options.