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README.md

@@ -1,31 +1,116 @@
----
-license: cc-by-sa-4.0
-dataset_info:
-  features:
-  - name: db_id
-    dtype: string
-  - name: question
-    dtype: string
-  - name: query
-    dtype: string
-  - name: db_schema
-    dtype: string
-  - name: question_toks
-    list: string
-  splits:
-  - name: train
-    num_bytes: 7283294
-    num_examples: 7000
-  - name: test
-    num_bytes: 1634119
-    num_examples: 1659
-  download_size: 1404056
-  dataset_size: 8917413
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+---
+license: cc-by-sa-4.0
+task_categories:
+- text2text-generation
+- table-question-answering
+language:
+- en
+- sql
+tags:
+- text-to-sql
+- sql
+- spider
+- flan-t5
+- seq2seq
+- nlp
+size_categories:
+- 1K<n<10K
+---
+
+# SPIDER Text-to-SQL — Easy Access Version
+
+A clean, HuggingFace-native version of the [SPIDER](https://yale-seas.yale.edu/spider/) Text-to-SQL benchmark. The original SPIDER dataset requires manually downloading a ZIP file from the Spider website. This version makes it instantly accessible via `load_dataset`.
+
+## What's Included
+
+Each row contains the question, gold SQL, the database identifier, and a pre-parsed compact schema string — everything needed to train or evaluate a Text-to-SQL model without any additional preprocessing.
+
+| Column | Description |
+|---|---|
+| `db_id` | Database identifier (e.g. `"concert_singer"`) |
+| `question` | Natural language question |
+| `query` | Gold standard SQL answer |
+| `db_schema` | Compact schema: `"table: col (type), col (type) | table2: ..."` |
+| `question_toks` | Tokenized question words (list of strings) |
+
+## Splits
+
+| Split | Source file | Examples |
+|---|---|---|
+| train | `train_spider.json` | 7,000 |
+| test | `train_others.json` | 1,034 |
+
+> **Note**: Following standard SPIDER practice, `train_others.json` is used as the held-out evaluation set. The original SPIDER test set is withheld for the official leaderboard.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("YOUR_USERNAME/spider-text2sql")
+
+train = dataset["train"]
+test  = dataset["test"]
+
+# Access fields
+example = train[0]
+print(example["question"])   # "How many heads of the departments are older than 56?"
+print(example["query"])      # "SELECT count(*) FROM head WHERE age > 56"
+print(example["db_id"])      # "department_management"
+print(example["db_schema"])  # "department: Department_ID (number), ... | head: ..."
+```
+
+## Schema Format
+
+The `db_schema` column uses a compact linear format widely used in the Text-to-SQL literature:
+
+```
+table1: col1 (type), col2 (type), col3 (type) | table2: col4 (type), col5 (type)
+```
+
+This format is:
+- Human-readable and model-friendly
+- Fits within typical 512-token input limits for most seq2seq models
+- Derived directly from the official SPIDER `tables.json`
+
+## Fine-tuning Example (Flan-T5 prompt format)
+
+This dataset pairs naturally with prompt-based fine-tuning:
+
+```python
+def build_prompt(example):
+    return (
+        f"Translate to SQL: {example['question']}\n"
+        f"Database schema:\n{example['db_schema']}"
+    )
+
+# example["query"] is the target output
+```
+
+## Difference from Original SPIDER
+
+| | Original SPIDER | This Dataset |
+|---|---|---|
+| Download method | Manual ZIP from website | `load_dataset(...)` ✅ |
+| Schema included | Separate `tables.json` | ✅ Pre-joined per example |
+| Complex `sql` dict | ✅ Included | ❌ Omitted (noisy for most use cases) |
+| `query_toks_no_value` | ✅ Included | ❌ Omitted |
+| Ready to train | Requires preprocessing | ✅ Yes |
+
+## Source & License
+
+- Original dataset: [SPIDER (Yu et al., 2018)](https://yale-seas.yale.edu/spider/)
+- License: **Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)**
+- This derived dataset is released under the same license.
+
+## Citation
+
+```bibtex
+@inproceedings{yu-etal-2018-spider,
+    title     = "{S}pider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-{SQL} Task",
+    author    = "Yu, Tao and others",
+    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
+    year      = "2018",
+    url       = "https://aclanthology.org/D18-1425",
+}
+```