TRL-Rbench

Row-level evaluation suite of TRL-Bench:

• row_prediction: 50 OpenML tables, 1.1M rows (887,720 train / 110,962 validation / 110,983 test) across 123 hand-verified targets. Per-row schema bundles features and targets as JSON dicts so all 50 tables share one config.
• record_linkage: 16 entity-matching sources (8 clean DeepMatcher + 4 dirty DeepMatcher + 4 WDC LSPM v2 sizes) unified into one config with 357,833 train / 95,817 validation / 42,835 test row pairs. Per-row stores both records as JSON dicts plus source / family / pair_id / label.

Schemas

row_prediction

{
  "openml_id": int32,                 # e.g., 3, 38, 458, ..., 46980 (50 distinct IDs)
  "dataset_name": string,             # e.g., "kc2"
  "row_idx": int32,                   # row index within the original OpenML CSV
  "record_json": string,              # JSON dict of feature columns
  "targets_json": string,             # JSON dict of target columns
  "target_specs_json": string,        # JSON list of {name, role, task_type}
  "dataset_metadata_json": string,    # full dataset.json copy (label_columns, splits, fingerprint)
}

To filter to one OpenML table:

from datasets import load_dataset
import json
ds = load_dataset("logo-lab/trl-rbench", "row_prediction")
ds_3 = ds.filter(lambda x: x["openml_id"] == 3)
print(json.loads(ds_3["train"][0]["record_json"]))

record_linkage

{
  "source": string,                   # e.g., "deepmatcher_beer", "wdc_products_xlarge"
  "family": string,                   # "deepmatcher_clean" | "deepmatcher_dirty" | "wdc_products"
  "pair_id": string,                  # f"{source}/{split}/{idx}"
  "table_a_record_json": string,      # JSON dict of left record
  "table_b_record_json": string,      # JSON dict of right record
  "label": int32,                     # 0 = non-match, 1 = match
}

Label-equivalent columns documented in the paper appendix (cluster_id, identifiers on WDC; class on Fodors-Zagats) are NOT removed at the parquet layer; users who want the leakage-free view should drop these keys after json.loads(record_json).

Quickstart

from datasets import load_dataset
import json

# Row prediction
rp = load_dataset("logo-lab/trl-rbench", "row_prediction")
print(rp)
sample = rp["train"][0]
print(json.loads(sample["record_json"]))
print(json.loads(sample["targets_json"]))
print(json.loads(sample["target_specs_json"]))

# Record linkage
rl = load_dataset("logo-lab/trl-rbench", "record_linkage")
sample = rl["train"][0]
print(sample["source"], sample["label"])
print(json.loads(sample["table_a_record_json"]))
print(json.loads(sample["table_b_record_json"]))

# Filter to one source
rl_beer = rl.filter(lambda x: x["source"] == "deepmatcher_beer")

See LICENSES.md for per-source license details.