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