Delete paper_classifier.py

paper_classifier.py

@@ -1,73 +0,0 @@
-from __future__ import annotations
-
-from typing import Iterable
-
-BASE_MODEL_NAME = "distilbert/distilbert-base-cased"
-DEFAULT_MODEL_DIR = "artifacts/distilbert-arxiv"
-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