add classic trec

README.md

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+---
+pretty_name: TREC Question Classification
+task_categories:
+  - text-classification
+language:
+  - en
+configs:
+  - config_name: default
+data_files:
+  train: data/train.parquet
+  validation: data/validation.parquet
+  test: data/test.parquet
+---
+
+# TREC
+
+A classic benchmark dataset for question classification with both coarse and fine-grained labels.
+
+- **Size:** small, clean, ready to use  
+- **Source:** [original release](https://cogcomp.seas.upenn.edu/Data/QA/QC/)  
+- **Format:** stored in Parquet  
+- **Compatibility:** 🧩 works with `datasets >= 4.0` (script loaders deprecated)  
+
+## Reference
+
+Li, X., & Roth, D. (2002).  
+*Learning Question Classifiers.*  
+[ACL Anthology](https://aclanthology.org/C02-1150/)  

data/metadata.json

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+{
+  "num_rows": {
+    "train": 4907,
+    "validation": 545,
+    "test": 500
+  },
+  "features": {
+    "text": "Value(dtype='string', id=None)",
+    "coarse_label": "Value(dtype='string', id=None)",
+    "coarse_description": "Value(dtype='string', id=None)",
+    "fine_label": "Value(dtype='string', id=None)",
+    "fine_description": "Value(dtype='string', id=None)"
+  },
+  "label_maps": {
+    "coarse_label": [
+      "ABBR",
+      "DESC",
+      "ENTY",
+      "HUM",
+      "LOC",
+      "NUM"
+    ],
+    "fine_label": [
+      "ABBR:abb",
+      "ABBR:exp",
+      "DESC:def",
+      "DESC:desc",
+      "DESC:manner",
+      "DESC:reason",
+      "ENTY:animal",
+      "ENTY:body",
+      "ENTY:color",
+      "ENTY:cremat",
+      "ENTY:currency",
+      "ENTY:dismed",
+      "ENTY:event",
+      "ENTY:food",
+      "ENTY:instru",
+      "ENTY:lang",
+      "ENTY:letter",
+      "ENTY:other",
+      "ENTY:plant",
+      "ENTY:product",
+      "ENTY:religion",
+      "ENTY:sport",
+      "ENTY:substance",
+      "ENTY:symbol",
+      "ENTY:techmeth",
+      "ENTY:termeq",
+      "ENTY:veh",
+      "ENTY:word",
+      "HUM:desc",
+      "HUM:gr",
+      "HUM:ind",
+      "HUM:title",
+      "LOC:city",
+      "LOC:country",
+      "LOC:mount",
+      "LOC:other",
+      "LOC:state",
+      "NUM:code",
+      "NUM:count",
+      "NUM:date",
+      "NUM:dist",
+      "NUM:money",
+      "NUM:ord",
+      "NUM:other",
+      "NUM:perc",
+      "NUM:period",
+      "NUM:speed",
+      "NUM:temp",
+      "NUM:volsize",
+      "NUM:weight"
+    ]
+  }
+}

data/test.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bfbf350bbe394d3586dd0a97807205f10e6586d0558f59ca42f0446d14e044ef
+size 17302

data/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f342ee3b9528a2fbb1543f1807d3b90cc1a78f6f7a2d706c8834c37b1a945b45
+size 200114

data/validation.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f5d0e78d62d1bf9a23a28760cff23e3d1677ef42266f933a31e414e2a21c48b9
+size 25867

preprocess.py

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+import argparse
+import json
+import random
+from pathlib import Path
+from typing import Optional, Sequence, TypedDict
+
+import requests
+from datasets import Dataset, DatasetDict
+
+OUT_DIR = Path(__file__).parent / "data"
+METADATA_PATH = OUT_DIR / "metadata.json"
+
+SEED = 42
+VAL_RATIO = 0.1
+
+URLS = {
+    "train": "https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label",
+    "test": "https://cogcomp.seas.upenn.edu/Data/QA/QC/TREC_10.label",
+}
+
+COARSE_DESC = {
+    "ABBR": "abbreviation", "ENTY": "entities", "DESC": "description and abstract concepts",
+    "HUM": "human beings", "LOC": "locations", "NUM": "numeric values"
+}
+
+FINE_DESC = {
+    "ABBR:abb":"abbreviation","ABBR:exp":"expression abbreviated",
+    "ENTY:animal":"animals","ENTY:body":"organs of body","ENTY:color":"colors","ENTY:cremat":"creative works",
+    "ENTY:currency":"currency names","ENTY:dismed":"diseases and medicine","ENTY:event":"events","ENTY:food":"food",
+    "ENTY:instru":"musical instrument","ENTY:lang":"languages","ENTY:letter":"letters like a-z","ENTY:other":"other entities",
+    "ENTY:plant":"plants","ENTY:product":"products","ENTY:religion":"religions","ENTY:sport":"sports",
+    "ENTY:substance":"elements and substances","ENTY:symbol":"symbols and signs","ENTY:techmeth":"techniques and methods",
+    "ENTY:termeq":"equivalent terms","ENTY:veh":"vehicles","ENTY:word":"words with a special property",
+    "DESC:def":"definition of something","DESC:desc":"description of something","DESC:manner":"manner of an action","DESC:reason":"reasons",
+    "HUM:gr":"a group/organization","HUM:ind":"an individual","HUM:title":"title of a person","HUM:desc":"description of a person",
+    "LOC:city":"cities","LOC:country":"countries","LOC:mount":"mountains","LOC:other":"other locations","LOC:state":"states",
+    "NUM:code":"codes","NUM:count":"counts","NUM:date":"dates","NUM:dist":"distances","NUM:money":"prices","NUM:ord":"ranks",
+    "NUM:other":"other numbers","NUM:period":"duration","NUM:perc":"percentages","NUM:speed":"speed","NUM:temp":"temperature",
+    "NUM:volsize":"size/area/volume","NUM:weight":"weight",
+}
+
+
+class TrecExample(TypedDict):
+    text: str
+    coarse_label: str
+    coarse_description: Optional[str]
+    fine_label: str
+    fine_description: Optional[str]
+
+
+def fetch(url: str) -> list[bytes]:
+    r = requests.get(url, timeout=30)
+    r.raise_for_status()
+    return r.content.splitlines()
+
+
+def parse(lines: Sequence[bytes]) -> list[TrecExample]:
+    rows: list[TrecExample] = []
+    for b in lines:
+        line = b.decode("utf-8", errors="replace").strip()
+        if not line or " " not in line:
+            continue
+        fine, text = line.split(" ", 1)
+        coarse = fine.split(":", 1)[0]
+        rows.append(
+            {
+                "text": text.strip(),
+                "coarse_label": coarse,
+                "coarse_description": COARSE_DESC.get(coarse, ""),
+                "fine_label": fine,
+                "fine_description": FINE_DESC.get(fine, ""),
+            }
+        )
+    return rows
+
+
+def extract_metadata(ds: DatasetDict) -> dict:
+    num_rows = {name: len(split) for name, split in ds.items()}
+    first_split = next(iter(ds.values()))
+    features = {name: repr(feat) for name, feat in first_split.features.items()}
+
+    coarse_labels = {label for split in ds.values() for label in split["coarse_label"]}
+    fine_labels = {label for split in ds.values() for label in split["fine_label"]}
+
+    label_maps = {
+        "coarse_label": sorted(coarse_labels),
+        "fine_label": sorted(fine_labels),
+    }
+
+    return {
+        "num_rows": num_rows,
+        "features": features,
+        "label_maps": label_maps}
+
+
+if __name__ == "__main__":
+    """Fetch TREC from source, split it, save as Parquet and add metadata.
+
+    Run: python preprocess_trec.py --val-ratio 0.1 --seed 42 --out-dir data
+    """
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--val-ratio", type=float, default=VAL_RATIO, help="Fraction of training set for validation")
+    ap.add_argument("--seed", type=int, default=SEED, help="Random seed for shuffling")
+    ap.add_argument("--out-dir", type=Path, help="Output directory for Parquet files")
+    ap.add_argument("--metadata-path", type=Path, help="Path for metadata.json")
+    args = ap.parse_args()
+
+    out_dir = args.out_dir or OUT_DIR
+    metadata_path = args.metadata_path or METADATA_PATH
+
+    train = parse(fetch(URLS["train"]))
+    test = parse(fetch(URLS["test"]))
+
+    rng = random.Random(args.seed)
+    rng.shuffle(train)
+    n_val = int(len(train) * args.val_ratio)
+    validation = train[:n_val]
+    train = train[n_val:]
+
+    data = DatasetDict(
+        {
+            "train": Dataset.from_list(train),
+            "validation": Dataset.from_list(validation),
+            "test": Dataset.from_list(test),
+        }
+    )
+
+    out_dir.mkdir(exist_ok=True, parents=True)
+    for name, split in data.items():
+        split.to_parquet(str(out_dir / f"{name}.parquet"))
+
+    metadata_path.write_text(json.dumps(extract_metadata(data), indent=2))