Update README.md

README.md

@@ -1,27 +1,75 @@
----
-dataset_info:
-  config_name: triplet
-  features:
-  - name: query
-    dtype: string
-  - name: positive
-    dtype: string
-  - name: negative
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 12581563.792427007
-    num_examples: 42076
-  - name: test
-    num_bytes: 3149278.207572993
-    num_examples: 10532
-  download_size: 1254810
-  dataset_size: 15730842.0
-configs:
-- config_name: triplet
-  data_files:
-  - split: train
-    path: triplet/train-*
-  - split: test
-    path: triplet/test-*
----
+---
+dataset_info:
+  config_name: triplet
+  features:
+  - name: query
+    dtype: string
+  - name: positive
+    dtype: string
+  - name: negative
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 12581563.792427007
+    num_examples: 42076
+  - name: test
+    num_bytes: 3149278.207572993
+    num_examples: 10532
+  download_size: 1254810
+  dataset_size: 15730842
+configs:
+- config_name: triplet
+  data_files:
+  - split: train
+    path: triplet/train-*
+  - split: test
+    path: triplet/test-*
+task_categories:
+- sentence-similarity
+---
+
+This dataset is the triplet subset of https://huggingface.co/datasets/sentence-transformers/sql-questions with a train and test split.
+
+The test split can be passed to [`TripletEvaluator`](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#tripletevaluator).
+
+The train and test spilts don't have any queries in common.
+
+<details>
+<summary>Here's the full script used to generate this dataset</summary>
+
+```python
+import os
+
+import datasets
+from sklearn.model_selection import train_test_split
+
+
+dataset = datasets.load_dataset(
+    "sentence-transformers/sql-questions", "triplet", split="train"
+)
+
+queries_unique = list({record["query"]: None for record in dataset})
+# Use a dict for deterministic (insertion) order
+len(queries_unique)
+
+queries_tr, queries_te = train_test_split(
+    queries_unique, test_size=0.2, random_state=42
+)
+
+queries_tr = set(queries_tr)
+queries_te = set(queries_te)
+train_dataset = dataset.filter(lambda record: record["query"] in queries_tr)
+test_dataset = dataset.filter(lambda record: record["query"] in queries_te)
+
+assert not set(train_dataset["query"]) & set(test_dataset["query"])
+assert len(train_dataset) + len(test_dataset) == len(dataset)
+
+
+dataset_dict = datasets.DatasetDict({"train": train_dataset, "test": test_dataset})
+dataset_dict.push_to_hub(
+    "aladar/sql-questions", config_name="triplet", token=os.environ["HF_TOKEN_CREATE"]
+)
+
+```
+
+</details>