| |
| """Run a GLiNER2 model over a split and write predictions for evaluate.py. |
| |
| The extraction schema (field label -> description) is read from the dataset's |
| own field_labels column, so no external file is needed. Every field is queried |
| at a low threshold; the evaluator selects the operating threshold afterwards. |
| Output parquet columns: doc_id, key, start, end, confidence, value. |
| |
| Usage: |
| python predict.py --model rntc/mc-bio-gliner-lymphome \ |
| --split validation --out preds_val.parquet |
| python predict.py --model rntc/mc-bio-gliner-lymphome \ |
| --split test --out preds_test.parquet |
| """ |
| import argparse |
| import json |
|
|
| import pandas as pd |
| import torch |
| from datasets import load_dataset |
| from gliner2 import GLiNER2 |
|
|
| DATASET = "rntc/lymphome-synth-v5-eval" |
| THRESHOLD = 0.001 |
| MAX_CHARS = 30000 |
|
|
|
|
| def build_schema(rows): |
| label2key, desc = {}, {} |
| for r in rows: |
| for key, meta in json.loads(r["field_labels"]).items(): |
| label2key[meta["label"]] = key |
| desc[meta["label"]] = meta["desc"] |
| return desc, label2key |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--model", required=True) |
| ap.add_argument("--split", default="test") |
| ap.add_argument("--out", required=True) |
| ap.add_argument("--dataset", default=DATASET) |
| ap.add_argument("--threshold", type=float, default=THRESHOLD) |
| ap.add_argument("--max_chars", type=int, default=MAX_CHARS) |
| a = ap.parse_args() |
|
|
| rows = list(load_dataset(a.dataset, split=a.split)) |
| desc, label2key = build_schema(rows) |
|
|
| model = GLiNER2.from_pretrained(a.model) |
| if torch.cuda.is_available(): |
| model = model.to("cuda") |
|
|
| out = [] |
| for i, d in enumerate(rows): |
| text = d["text"][:a.max_chars] |
| try: |
| with torch.no_grad(): |
| res = model.extract_entities(text, desc, threshold=a.threshold, |
| include_confidence=True, include_spans=True) |
| except Exception: |
| continue |
| ents = res.get("entities", res) if isinstance(res, dict) else {} |
| for label, mentions in (ents.items() if isinstance(ents, dict) else []): |
| key = label2key.get(label) |
| if not key or not isinstance(mentions, list): |
| continue |
| for m in mentions: |
| if not isinstance(m, dict) or "start" not in m or "end" not in m: |
| continue |
| s, e = int(m["start"]), int(m["end"]) |
| out.append({"doc_id": d["id"], "key": key, "start": s, "end": e, |
| "confidence": float(m.get("confidence", 0.0)), |
| "value": m.get("text", text[s:e])}) |
| if (i + 1) % 50 == 0: |
| print(f" {i + 1}/{len(rows)} docs, {len(out)} preds", flush=True) |
|
|
| pd.DataFrame(out).to_parquet(a.out) |
| print(f"wrote {len(out)} predictions -> {a.out}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
