Update README.md

README.md

@@ -1,4 +1,16 @@
 ---
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+tags:
+- sentence-transformers
+task_categories:
+- feature-extraction
+- sentence-similarity
+pretty_name: SQL Questions
 dataset_info:
 - config_name: mined-negative
   features:
@@ -72,3 +84,62 @@ configs:
   - split: train
     path: triplet/train-*
 ---
+
+# Dataset card for SQL Questions
+
+This dataset is a reformatting of the [`sql_questions_triplets`](https://huggingface.co/datasets/sergeyvi4ev/sql_questions_triplets) dataset by [@sergeyvi4ev](https://huggingface.co/sergeyvi4ev), such that the dataset can be directly used to train Sentence Transformer models.
+
+## Dataset Subsets
+
+### `pair` subset
+
+* Columns: "query", "positive"
+* Column types: `str`, `str`
+* Examples:
+    ```python
+    {
+      'query': 'How many zip codes are under Barre, VT?',
+      'positive': '"Barre, VT" is the CBSA_name',
+    }
+    ```
+* Collection strategy: Reading the SQL Questions dataset and selecting all query-positive pairs.
+* Deduplified: Yes
+
+### `triplet` subset
+
+* Columns: "query", "positive", "negative"
+* Column types: `str`, `str`, `str`
+* Examples:
+    ```python
+    {
+      'query': 'How many zip codes are under Barre, VT?',
+      'positive': '"Barre, VT" is the CBSA_name',
+      'negative': "coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'"
+    }
+    ```
+* Collection strategy: Reading the SQL Questions dataset and selecting all possible triplet pairs.
+* Deduplified: No
+
+### `mined-negative` subset
+
+* Columns: "query", "positive", "negative_1", "negative_2", "negative_3", "negative_4", "negative_5", "negative_6", "negative_7", "negative_8", "negative_9", "negative_10"
+* Column types: `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`
+* Examples:
+    ```python
+    {
+      "query": "How many zip codes are under Barre, VT?",
+      "positive": "\"Barre, VT\" is the CBSA_name",
+      "negative_1": "coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'",
+      "negative_2": "name of county refers to county",
+      "negative_3": "median age over 40 refers to median_age > 40",
+      "negative_4": "\"PHILLIPS\" is the county; 'Montana' is the name of state",
+      "negative_5": "name of the CBSA officer refers to CBSA_name; position of the CBSA officer refers to CBSA_type;",
+      "negative_6": "population greater than 10000 in 2010 refers to population_2010 > 10000;",
+      "negative_7": "postal points refer to zip_code; under New York-Newark-Jersey City, NY-NJ-PA refers to CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA';",
+      "negative_8": "the largest water area refers to MAX(water_area);",
+      "negative_9": "\"Wisconsin\" is the state; largest land area refers to Max(land_area); full name refers to first_name, last_name; postal code refers to zip_code",
+      "negative_10": "\"Alabama\" and \"Illinois\" are both state; Ratio = Divide (Count(state = 'Alabama'), Count(state = 'Illinois'))"
+    }
+    ```
+* Collection strategy: Reading the SQL Questions dataset, filtering away the 15 samples that did not have 10 negative pairs, and formatting them in the described columns.
+* Deduplified: No