Update README.md

README.md

@@ -1,3 +1,155 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+---
+---
+datasets:
+- Voice49/dber
+pretty_name: DB-ER — Dataset for Database Entity Recognition
+language:
+- en
+tags:
+- db-er
+- schema-linking
+- text-to-sql
+- ner
+- token-classification
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+size_categories:
+- 10K<n<100K
+license: other
+---
+
+# DB-ER — Dataset for Database Entity Recognition
+
+## Dataset Summary
+**DB-ER** is a token-level dataset for **Database Entity Recognition (DB-ER)** in **natural-language queries (NLQs)** paired with SQL. The task is to tag each token as one of **Table**, **Column**, **Value**, or **O** (non-entity).  
+Each example includes: the NLQ, database identifier, a canonical dataset id, the paired SQL query, a tokenized question, a compact **entity→token** reverse index, an explicit **entities** table (typed schema/value items), and CoNLL-style **DB‑ER tags**.
+
+---
+
+## What’s inside
+
+### Labels
+- **4-class:** `Table`, `Column`, `Value`, `O`  
+
+### Fields per example
+- `question_id` *(int)* — Example id
+- `db_id` *(str)* — Database identifier
+- `dber_id` *(str)* — Canonical id linking back to the source file/split (BIRD, SPIDER)
+- `question` *(str)* — NLQ text
+- `SQL` *(str)* — Paired SQL query
+- `tokens` *(List[str])* — Tokenized NLQ
+- `entities` *(List[Object])* — Typed DB items referenced in the SQL; each item has:
+  - `id` *(int)* — Local entity id (unique within the example)
+  - `type` *("table"|"column"|"value")*
+  - `value` *(str)* — Surface form from the DB schema or literal value
+- `entity_to_token` *(Dict[str, List[int]])* — Mapping entity to token
+- `dber_tags` *(List[str])* — **CoNLL-style IOB2** tags over `tokens`
+
+---
+
+## Splits
+**Entity token prevalence is consistent across splits: ~29% entity vs. ~71% `O`.**
+
+| Split             | # Examples |
+|-------------------|-----------:|
+| `human_train`     | **500**  |
+| `human_test`      | **500**    |
+| `synthetic_train` | **15,026** |
+
+`synthetic_train` is produced via our **auto-annotation pipeline**, which aligns SQL-referenced entities to NLQ spans using string-similarity candidates (Jaccard 3-gram / Levenshtein) and a **non-overlapping ILP** selection objective. See **Annotation** below.
+
+---
+
+## Example instances
+```json
+{
+  "question_id": 13692,
+  "db_id": "retail_complains",
+  "dber_id": "bird:train.json:282",
+  "question": "Among the clients born between 1980 and 2000, list the name of male clients who complained through referral.",
+  "SQL": "SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.year BETWEEN 1980 AND 2000 AND T1.sex = 'Male' AND T2.`Submitted via` = 'Referral'",
+  "tokens": ["Among","the","clients","born","between","1980","and","2000",",","list","the","name","of","male","clients","who","complained","through","referral","."],
+  "entities": [
+    {"id":0,"type":"column","value":"first"},
+    {"id":1,"type":"column","value":"middle"},
+    {"id":2,"type":"column","value":"last"},
+    {"id":3,"type":"table","value":"client"},
+    {"id":4,"type":"table","value":"events"},
+    {"id":5,"type":"column","value":"client_id"},
+    {"id":6,"type":"column","value":"year"},
+    {"id":7,"type":"value","value":"1980"},
+    {"id":8,"type":"value","value":"2000"},
+    {"id":9,"type":"column","value":"sex"}
+    {"id":10,"type":"value","value":"Male"},
+    {"id":11,"type":"column","value":"Submitted via"},
+    {"id":12,"type":"value","value":"Referral"},
+  ],
+  "entity_to_token": {"3":[2],"7":[5],"8":[7],"10":[13],"5":[14],"12":[18]},
+  "dber_tags": ["O","O","B-TABLE","O","O","B-VALUE","O","B-VALUE","O","O","O","O","O","B-VALUE","B-COLUMN","O","O","O","B-VALUE","O"]
+}
+```
+
+---
+
+## How to load
+
+### Load JSONL files
+```python
+from datasets import load_dataset
+
+data_files = {
+    "human_train": "https://huggingface.co/datasets/Voice49/dber/resolve/main/human_train.jsonl",
+    "human_test": "https://huggingface.co/datasets/Voice49/dber/resolve/main/human_test.jsonl",
+    "synthetic_train": "https://huggingface.co/datasets/Voice49/dber/resolve/main/synthetic_train.jsonl",
+}
+ds = load_dataset("json", data_files=data_files)
+print(ds)
+print(ds["human_train"][0])
+```
+
+### Load from the Hub
+```python
+from datasets import load_dataset
+ds = load_dataset("Voice49/dber")
+```
+
+---
+
+## Annotation (human + synthetic)
+- **Human**: collaborative web UI with schema and SQL visible during labeling.
+- **Synthetic**: for each NLQ–SQL pair, generate candidate spans with Jaccard/Levenshtein, then solve a **non-overlapping ILP** to select spans maximizing similarity. Hyperparameters are validated on human data.
+
+---
+
+## Data provenance
+- **Sources:** text-to-SQL benchmarks BIRD (https://bird-bench.github.io/) and Spider (https://yale-lily.github.io/spider).
+- **Transform:** NLQ–SQL pairs → DB-ER annotations via the synthetic pipeline; human annotations provide gold labels and validation.
+
+<!-- ---
+
+## Licensing
+- **License:** `other` (see the repository `LICENSE` for terms). Research use only unless otherwise permitted.
+
+---
+
+## Citation
+If you use **DB-ER**, please cite:
+
+```bibtex
+@inproceedings{fu2025dber,
+  title     = {Database Entity Recognition with Data Augmentation and Deep Learning},
+  author    = {Zikun Fu and Chen Yang and Kourosh Davoudi and Ken Q. Pu},
+  booktitle = {Proc. IEEE International Conference on Information Reuse and Integration for Data Science (IRI)},
+  address   = {San Jose, CA, USA},
+  year      = {2025}
+}
+``` -->
+
+---
+
+## Release notes
+- **v1.0:** Initial public release