EVAL.md

# Evaluating on HandleAtlas-benchmark

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

---

## 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.

---

## Setup

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

```bash
# 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.

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

---

## Quickstart

### Evaluate HandleAtlas (PyTorch float)

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

### Evaluate the CPU (INT8 ONNX) variant

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

### Evaluate base GLiNER zero-shot

```bash
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.

```bash
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:

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

---

## 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 |

---

## Adding your own model

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

```python
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:

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

That's it — scoring and reporting are reused.

---

## 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.

---

## 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 |