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import json
import sys
from pathlib import Path
from typing import Any, Dict, List
DEEP_CITATION_ROOT = Path(__file__).resolve().parents[2] / "Deep-Citation"
if not DEEP_CITATION_ROOT.exists():
raise SystemExit(f"Deep-Citation repo not found at {DEEP_CITATION_ROOT}")
sys.path.insert(0, str(DEEP_CITATION_ROOT))
from data import CollateFn, create_data_channels
from Model import MultiHeadLanguageModel
import torch
from torch.utils.data import DataLoader
PAPER_META_FILE = "paper_metadata.json"
USAGE_CONTEXTS_FILE = "usage_contexts.json"
OUT_FILE = "usage_context_labels.json"
LABEL_SET = [
"Background",
"Uses",
"Extends",
"CompareOrContrast",
"Motivation",
"Future",
]
def load_json(path: Path) -> Any | None:
if not path.exists():
return None
try:
return json.loads(path.read_text(encoding="utf-8"))
except Exception:
return None
def iter_paper_dirs(root: Path) -> List[Path]:
out: List[Path] = []
for child in root.iterdir():
if child.is_dir() and (child / PAPER_META_FILE).exists():
out.append(child)
return out
def flatten_contexts(usage: Dict[str, Any]) -> List[Dict[str, Any]]:
contexts: List[Dict[str, Any]] = []
idx = 1
for entry in usage.get("citing_papers", []) or []:
if not isinstance(entry, dict):
continue
citing_title = entry.get("title") or "Unknown citing paper"
citing_paper_id = entry.get("citing_paper_id") or ""
for c in entry.get("contexts", []) or []:
if not isinstance(c, dict):
continue
text = (c.get("text") or "").strip()
if not text:
continue
contexts.append(
{
"id": idx,
"text": text,
"citing_title": citing_title,
"citing_paper_id": citing_paper_id,
}
)
idx += 1
return contexts
def _resolve_model_name(lm: str) -> str:
if lm == "scibert":
return "allenai/scibert_scivocab_uncased"
if lm == "bert":
return "bert-base-uncased"
if lm == "deberta":
return "microsoft/deberta-v3-base"
if lm == "deberta-large":
return "microsoft/deberta-v3-large"
return lm
def _infer_head_sizes(state_dict: Dict[str, Any]) -> List[int]:
head_weights = [
(k, v) for k, v in state_dict.items() if k.startswith("lns.") and k.endswith(".weight")
]
head_weights.sort(key=lambda x: int(x[0].split(".")[1]))
return [int(weight.shape[0]) for _, weight in head_weights]
class _ContextDataset:
def __init__(self, texts: List[str]):
self.texts = texts
def __len__(self) -> int:
return len(self.texts)
def __getitem__(self, idx: int):
return (self.texts[idx], torch.tensor(0), torch.tensor(0))
def label_with_model(
contexts: List[Dict[str, Any]],
model_path: Path,
data_dir: Path,
class_definition: Path,
lm: str,
device: str,
batch_size: int,
) -> Dict[int, Dict[str, Any]]:
data_file = data_dir / "acl.tsv"
train_data, _, _, label_names = create_data_channels(
str(data_file),
str(class_definition),
lmbd=1.0,
)
modelname = _resolve_model_name(lm)
state_dict = torch.load(model_path, map_location=device)
head_sizes = _infer_head_sizes(state_dict)
model = MultiHeadLanguageModel(
modelname=modelname,
device=device,
readout="ch",
num_classes=head_sizes,
).to(device)
model.load_state_dict(state_dict)
model.eval()
collate_fn = CollateFn(
modelname=modelname,
class_definitions=train_data.class_definitions,
instance_weights=False,
)
dataset = _ContextDataset([ctx["text"] for ctx in contexts])
loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn)
outputs: Dict[int, Dict[str, Any]] = {}
idx_offset = 0
with torch.no_grad():
for batched_text, labels, ds_indices, class_tokens, class_ds_indices in loader:
ds_indices = ds_indices.to(device)
class_ds_indices = class_ds_indices.to(device)
logits = model(batched_text, ds_indices, class_tokens, class_ds_indices)[0]
probs = torch.softmax(logits, dim=1)
preds = logits.argmax(dim=1).cpu().tolist()
pred_confidences = probs.max(dim=1).values.cpu().tolist()
top2 = torch.topk(probs, k=2, dim=1).values.cpu()
margins = (top2[:, 0] - top2[:, 1]).tolist()
for i, pred in enumerate(preds):
raw_label = label_names[pred]
outputs[idx_offset + i + 1] = {
"id": idx_offset + i + 1,
"label": raw_label,
"confidence": float(pred_confidences[i]),
"confidence_margin": float(margins[i]),
"cue_span": "",
"rationale": "scibert_model",
}
idx_offset += len(preds)
return outputs
def aggregate_citing_labels(labels: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
by_citing: Dict[str, List[Dict[str, Any]]] = {}
for item in labels:
citing_id = item.get("citing_paper_id") or ""
by_citing.setdefault(citing_id, []).append(item)
aggregated: List[Dict[str, Any]] = []
for citing_id, items in by_citing.items():
title = items[0].get("citing_title", "")
labels_set = {it.get("label") for it in items}
if "Extends" in labels_set:
label = "Extends"
evidence_ids = [it["id"] for it in items if it.get("label") == "Extends"]
elif "Uses" in labels_set:
label = "Uses"
evidence_ids = [it["id"] for it in items if it.get("label") == "Uses"]
elif "CompareOrContrast" in labels_set:
label = "CompareOrContrast"
evidence_ids = [
it["id"] for it in items if it.get("label") == "CompareOrContrast"
]
else:
label = "Background"
evidence_ids = []
aggregated.append(
{
"citing_paper_id": citing_id,
"citing_title": title,
"label": label,
"evidence_context_ids": evidence_ids,
}
)
return aggregated
def aggregate_final_label(citing_labels: List[Dict[str, Any]]) -> str:
labels_set = {item.get("label") for item in citing_labels}
if "Extends" in labels_set:
return "Extends"
if "Uses" in labels_set:
return "Uses"
if "CompareOrContrast" in labels_set:
return "CompareOrContrast"
return "Background"
def score_for_paper(
paper_dir: Path,
batch_size: int,
overwrite: bool,
model_path: Path,
model_data_dir: Path,
model_class_def: Path,
model_lm: str,
device: str,
) -> str:
usage_path = paper_dir / USAGE_CONTEXTS_FILE
usage = load_json(usage_path)
if not isinstance(usage, dict):
return "missing_usage"
contexts = flatten_contexts(usage)
if not contexts:
return "empty_contexts"
out_path = paper_dir / OUT_FILE
if out_path.exists() and not overwrite:
return "skipped"
labeled = label_with_model(
contexts=contexts,
model_path=model_path,
data_dir=model_data_dir,
class_definition=model_class_def,
lm=model_lm,
device=device,
batch_size=batch_size,
)
labels_sorted = []
for context in contexts:
context_id = context["id"]
item = labeled.get(context_id)
if not item:
item = {
"id": context_id,
"label": "Background",
"confidence": 0.0,
"cue_span": "",
"rationale": "missing label",
}
item = dict(item)
item["citing_paper_id"] = context.get("citing_paper_id", "")
item["citing_title"] = context.get("citing_title", "")
item["text"] = context.get("text", "")
labels_sorted.append(item)
citing_labels = aggregate_citing_labels(labels_sorted)
payload = {
"paper_id": usage.get("paper_id"),
"num_contexts": len(contexts),
"label_set": LABEL_SET,
"labels": labels_sorted,
"citing_paper_labels": citing_labels,
"final_label": aggregate_final_label(citing_labels),
}
out_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
return "labeled"
def main() -> None:
parser = argparse.ArgumentParser(
description="Label citation functions using a Deep-Citation checkpoint."
)
parser.add_argument(
"--root",
type=str,
default="runs/processed_papers",
help="Root directory containing processed paper directories.",
)
parser.add_argument(
"--batch-size",
type=int,
default=32,
help="Batch size for model inference.",
)
parser.add_argument(
"--overwrite",
action="store_true",
help="Overwrite existing usage_context_labels.json files.",
)
parser.add_argument(
"--model-path",
type=str,
required=True,
help="Path to Deep-Citation best_model.pt checkpoint.",
)
parser.add_argument(
"--model-data-dir",
type=str,
default="Deep-Citation/Data",
help="Deep-Citation data directory (for label order).",
)
parser.add_argument(
"--model-class-def",
type=str,
default="Deep-Citation/Data/class_def.json",
help="Deep-Citation class_def.json path.",
)
parser.add_argument(
"--model-lm",
type=str,
default="scibert",
help="Model name used for the Deep-Citation checkpoint.",
)
parser.add_argument(
"--device",
type=str,
default="cuda",
help="Device for model inference (cuda/cpu).",
)
args = parser.parse_args()
model_path = Path(args.model_path).expanduser().resolve()
if not model_path.exists():
raise SystemExit(f"Model path does not exist: {model_path}")
root = Path(args.root).expanduser().resolve()
if not root.exists():
raise SystemExit(f"Root directory does not exist: {root}")
paper_dirs = sorted(iter_paper_dirs(root), key=lambda p: p.name)
print(f"[INFO] Found {len(paper_dirs)} paper dirs under {root}")
counts = {
"labeled": 0,
"skipped": 0,
"missing_usage": 0,
"empty_contexts": 0,
}
for paper_dir in paper_dirs:
status = score_for_paper(
paper_dir,
args.batch_size,
args.overwrite,
model_path=model_path,
model_data_dir=Path(args.model_data_dir).expanduser().resolve(),
model_class_def=Path(args.model_class_def).expanduser().resolve(),
model_lm=args.model_lm,
device=args.device,
)
counts[status] = counts.get(status, 0) + 1
print(f"[{status.upper()}] {paper_dir.name}")
print(
"[SUMMARY] labeled={labeled}, skipped={skipped}, missing_usage={missing_usage}, "
"empty_contexts={empty_contexts}".format(**counts)
)
if __name__ == "__main__":
main()
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