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--- |
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language: |
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- en |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- feature-extraction |
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- sentence-similarity |
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pretty_name: Coco Captions |
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tags: |
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- sentence-transformers |
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dataset_info: |
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config_name: pair |
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features: |
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- name: caption1 |
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dtype: string |
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- name: caption2 |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 46793540 |
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num_examples: 414010 |
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download_size: 23935511 |
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dataset_size: 46793540 |
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configs: |
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- config_name: pair |
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data_files: |
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- split: train |
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path: pair/train-* |
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--- |
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# Dataset Card for Coco Captions |
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This dataset is a collection of caption pairs given to the same image, collected from the Coco dataset. See [Coco](https://cocodataset.org/) for additional information. |
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This dataset can be used directly with Sentence Transformers to train embedding models. |
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Note that two captions for the same image do not strictly have the same semantic meaning. |
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## Dataset Subsets |
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### `pair` subset |
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* Columns: "caption1", "caption2" |
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* Column types: `str`, `str` |
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* Examples: |
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```python |
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{ |
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'caption1': 'A clock that blends in with the wall hangs in a bathroom. ', |
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'caption2': 'A very clean and well decorated empty bathroom', |
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} |
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``` |
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* Collection strategy: Reading the Coco Captions dataset from [embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data), which has lists of duplicate captions. I've considered all adjacent captions as a positive pair, plus the last and first caption. So, e.g. 5 duplicate captions results in 5 duplicate pairs. |
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* Deduplified: No |
