src/paper_classifier.py

from __future__ import annotations

from typing import Iterable
from pathlib import Path


BASE_MODEL_NAME = "distilbert/distilbert-base-cased"
DEFAULT_MODEL_DIR = Path(__file__).resolve().parent.parent
MAX_LENGTH = 256
TOP_P_THRESHOLD = 0.95

EXPECTED_ARXIV_CATEGORIES = [
    "Computer Science",
    "Physics",
    "Mathematics",
    "Statistics",
    "Quantitative Biology",
    "Quantitative Finance",
    "Economics",
    "Electrical Engineering and Systems Science",
]
EXAMPLES = {
    "Graph Neural Networks": {
        "title": "Message Passing Neural Networks for Molecular Property Prediction",
        "abstract": (
            "We introduce a graph-based neural architecture for supervised learning on "
            "molecular graphs. The model propagates messages between atoms, aggregates "
            "node states into a graph embedding, and predicts physical and chemical "
            "properties with competitive accuracy."
        ),
    },
    "Physics": {
        "title": "Topological phase transitions in two-dimensional quantum materials",
        "abstract": (
            "We study a lattice model with strong spin-orbit coupling and show how "
            "interactions modify the phase diagram. Using numerical simulations we "
            "characterize edge states, quantify transport signatures, and discuss "
            "observable consequences for low-temperature experiments."
        ),
    },
    "Bioinformatics": {
        "title": "Transformer models for protein function annotation from sequence",
        "abstract": (
            "We pretrain a transformer encoder on amino acid sequences and finetune it "
            "for protein function prediction. The approach improves annotation quality "
            "for underrepresented families and reveals biologically meaningful sequence "
            "patterns."
        ),
    },
}


def format_input_text(title: str, abstract: str) -> str:
    title = title.strip()
    abstract = abstract.strip()

    parts: list[str] = []
    if title:
        parts.append(f"Title: {title}\nTitle summary: {title}")
    if abstract:
        parts.append(f"Abstract: {abstract}")

    return "\n\n".join(parts)


def take_top_p(records: Iterable[dict[str, float]], threshold: float) -> list[dict[str, float]]:
    selected: list[dict[str, float]] = []
    cumulative = 0.0

    for record in records:
        selected.append(record)
        cumulative += record["score"]
        if cumulative >= threshold:
            break

    return selected