EVAL.md

Evaluating on HandleAtlas-benchmark

A self-contained eval harness for the LumeData/HandleAtlas-benchmark dataset. The whole protocol — splits, labels, thresholds, scoring — lives in eval.yaml; the runner in run_eval.py reads that file, loads the dataset from the Hub, and reports the same numbers used on the model cards.

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What gets measured

metric	definition
span-only F1 (primary)	label-agnostic; a prediction matches a gold span when their character-offset IoU ≥ 0.5
span+label F1	exact match on (start, end, label) — only fair when models share this taxonomy
latency	wall-clock ms per record, batch=1, CPU

Per-label P/R/F1 is also printed for diagnostic use.

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Setup

You only need the deps for the adapter you plan to use.

# core (always needed)
pip install datasets pyyaml

# adapter: gliner  (GLiNER models, including HandleAtlas)
pip install gliner "transformers<5" torch
#   + ONNX (only if you pass --onnx):
pip install onnx onnxruntime optimum "accelerate>=0.26"

# adapter: hf-pipeline  (BERT/DeBERTa token-classification models)
pip install "transformers<5" torch
#   Some PII models (e.g. openai/privacy-filter) require a newer transformers:
pip install "git+https://github.com/huggingface/transformers.git"

Recommended: use uv so each run gets an isolated env.

uv run --with gliner --with 'transformers<5' --with torch \
       --with datasets --with pyyaml \
       python run_eval.py --adapter gliner --model LumeData/HandleAtlas-166m

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Quickstart

Evaluate HandleAtlas (PyTorch float)

python run_eval.py --adapter gliner --model LumeData/HandleAtlas-166m

Evaluate the CPU (INT8 ONNX) variant

python run_eval.py \
  --adapter gliner \
  --model LumeData/HandleAtlas-166m-CPU \
  --onnx

Evaluate base GLiNER zero-shot

python run_eval.py --adapter gliner --model urchade/gliner_small-v2.1

Evaluate a different-taxonomy PII model (label-agnostic only)

The span-only F1 is fair across taxonomies; the span+label F1 will read low — that's expected.

python run_eval.py \
  --adapter hf-pipeline \
  --model openai/privacy-filter

If you want span+label to be meaningful, pass --label-map to translate the model's classes into the HandleAtlas label set:

python run_eval.py \
  --adapter hf-pipeline \
  --model some/pii-model \
  --label-map "USERNAME=generic_username,SOCIAL=instagram_username"

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CLI flags

flag	what it does
--adapter	gliner or hf-pipeline (required)
--model	HF Hub id or local path; passed straight to the adapter (required)
--spec	path to eval.yaml (defaults to the one next to run_eval.py)
--threshold	override the decoding threshold from the spec
--label-map	model_label=target_label,... for hf-pipeline
--onnx, --onnx-file	load the ONNX file instead of the PyTorch weights (GLiNER)
--threads	CPU threads (default 8)
--device	hf-pipeline: -1=CPU, 0=first GPU
--limit N	only score the first N records (smoke test)
--save-predictions PATH	write per-record predictions to JSONL
--json	also dump the metrics JSON at the end

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Adding your own model

If neither built-in adapter fits, subclass Adapter in run_eval.py and register it. The contract is one method:

class MyAdapter(Adapter):
    name = "mymodel"

    def __init__(self, model, labels, threshold, per_label_thresholds, **_):
        super().__init__(model, labels, threshold, per_label_thresholds)
        # load once
        self.m = my_lib.load(model)

    def predict(self, text: str) -> list[dict]:
        # MUST return [{"start": int, "end": int, "label": str}, ...]
        # `start`/`end` are Python character offsets, end exclusive.
        return [{"start": s, "end": e, "label": l}
                for s, e, l in self.m.extract(text, self.labels)]

ADAPTERS["mymodel"] = MyAdapter

Then:

python run_eval.py --adapter mymodel --model path/or/id

That's it — scoring and reporting are reused.

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Reproducing the published numbers

The values printed by this harness on --model LumeData/HandleAtlas-166m should match the baselines table in eval.yaml. If they don't, check:

1. Threshold — the spec sets 0.5 with a 0.65 override on generic_username. Don't override unless you mean to.
2. Split — the harness only uses the test split; the published numbers are on this same split with seed=123.
3. Hardware — latency was measured on a MacBook Pro M5 Pro at 8 threads. Different hardware will give different ms numbers; F1 should not move.

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Files

file	purpose
eval.yaml	declarative protocol — dataset, labels, thresholds, metrics, baselines
run_eval.py	runner — loads dataset, calls the adapter, scores, reports
EVAL.md	this file
test.jsonl	the actual eval data, also mirrored on the Hub
README.md	dataset card