README.md

---
language:
- en
license: cc-by-sa-4.0
license_link: https://huggingface.co/datasets/logo-lab/trl-dlte/blob/main/LICENSES.md
pretty_name: TRL-DLTE
tags:
- tabular
- data-lake
- table-discovery
- table-enrichment
- benchmark
- representation-learning
- trl-bench
size_categories:
- 100K<n<10M
configs:
- config_name: manifests
  data_files:
  - {split: train, path: data/manifests/train-*.parquet}
- config_name: lake
  data_files:
  - {split: train, path: data/lake/train-*.parquet}
- config_name: table_maps
  data_files:
  - {split: train, path: data/table_maps/train-*.parquet}
---

# TRL-DLTE

Compositional **Data-Lake Table Enrichment** suite of TRL-Bench. A 47,772-table data lake derived from 1,379 TabFact and WikiTableQuestions parent tables, fragmented at four cumulative noise tiers (clean / schema / cell / hard). Each parent yields a seed query, a union target (additional rows), and a join target (additional columns); the system must recover both targets from the lake by retrieval, column alignment, and row matching.

This repository ships two configs:

- **`manifests`**: lightweight metadata records (109,336 rows, ~900 KB) — every entity in the benchmark (parents, fragments, lake tables, query tasks, distractor IDs, splits, fragmentation config) under a unified `(record_type, record_json)` schema.
- **`lake`**: the full **54,667-table** content (1,379 parents + 16,548 fragments [5,516 query_seed + 5,516 target_union + 5,516 target_join] + 36,740 distractors), each with `csv_text` + structural metadata + provenance keys. The retriever's actual search space is the **47,772-table** subset (`target_union + target_join + distractor`); parents and query seeds are released alongside as pipeline I/O.

## `manifests` schema

```python
{
  "record_type": string,    # "parent" | "fragment" | "lake_table" | "split_assignment" |
                            # "query_task" | "ckan_distractor" | "fragmentation_config" |
                            # "gt_validation"
  "record_json": string,    # JSON-encoded record content
}
```

Counts:

| record_type | rows |
|---|---|
| `lake_table`           | 47,772 |
| `ckan_distractor`      | 36,740 |
| `fragment`             | 16,548 |
| `query_task`           | 5,516  |
| `parent`               | 1,379  |
| `split_assignment`     | 1,379  |
| `fragmentation_config` | 1      |
| `gt_validation`        | 1      |

## `lake` schema

```python
{
  "table_id": string,
  "kind": string,           # "parent_tabfact" | "parent_wtq" | "target_union" |
                            # "target_join" | "query_seed" | "distractor"
  "parent_id": string,      # empty for distractors
  "parent_source": string,  # "tabfact" | "wtq" | ""
  "noise_tier": int32,      # 0/1/2/3 for fragments; -1 for parents/distractors
  "fragment_type": string,  # "seed" | "union" | "join" | ""
  "split": string,          # "train" | "dev" | "test" | "" (distractors are unsplit)
  "csv_text": string,       # raw CSV content
  "n_rows": int32,
  "n_cols": int32,
}
```

## `table_maps` schema

Per-fragment column-mapping records (16,548 rows; one per fragment table_id).
Encodes the row-index and column-index back-pointers from each fragment to its
parent table. Required for Stage-2 (alignment) and Stage-3 (merge) evaluation;
the retrieval-only Stage-1 does NOT require these.

```python
{
  "table_id": string,                # fragment id (matches `lake` table_id)
  "row_parent_idx_json": string,     # JSON-encoded list[int]: per-row parent-row idx
  "col_parent_idx_json": string,     # JSON-encoded list[int]: per-col parent-col idx
}
```

Original on-disk form is an `.npz` per table with two int32 arrays
(`row_parent_idx`, `col_parent_idx`). The stager
(`trl_bench.data.stage._stage_dlte_task`) reconstructs the `.npz` files from
this config.

## Quickstart

```python
from datasets import load_dataset
import json

# Manifests (structural metadata)
m = load_dataset("logo-lab/trl-dlte", "manifests")
print(m["train"]["record_type"][:5])
parents = m["train"].filter(lambda x: x["record_type"] == "parent")
print(json.loads(parents[0]["record_json"]))   # full parent record dict

# Lake content
lake = load_dataset("logo-lab/trl-dlte", "lake")
parents = lake["train"].filter(lambda x: x["kind"].startswith("parent_"))
distractors = lake["train"].filter(lambda x: x["kind"] == "distractor")
union_targets = lake["train"].filter(lambda x: x["kind"] == "target_union")
```

## License

Umbrella **CC-BY-SA-4.0** (driven by 390 WTQ-derived parents). Per-component licenses in `LICENSES.md`. Manifest records and the fragmentation config are TRL-Bench-derived under CC-BY-4.0; the lake content inherits per-source upstream licenses (TabFact: CC-BY 4.0; WTQ: CC-BY-SA 4.0; CKAN distractors: CC-BY 4.0 record / per-portal upstream).