Dataset Card for "coco_captions"

Dataset Summary

COCO is a large-scale object detection, segmentation, and captioning dataset. This repo contains five captions per image; useful for sentence similarity tasks.

Disclaimer: The team releasing COCO did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.

Supported Tasks

• Sentence Transformers training; useful for semantic search and sentence similarity.

Languages

• English.

Dataset Structure

Each example in the dataset contains quintets of similar sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value":

{"set": [sentence_1, sentence_2, sentence3, sentence4, sentence5]}
{"set": [sentence_1, sentence_2, sentence3, sentence4, sentence5]}
...
{"set": [sentence_1, sentence_2, sentence3, sentence4, sentence5]}

This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences.

Usage Example

Install the ? Datasets library with pip install datasets and load the dataset from the Hub with:

from datasets import load_dataset
dataset = load_dataset("embedding-data/coco_captions")

The dataset is loaded as a DatasetDict and has the format:

DatasetDict({
    train: Dataset({
        features: ['set'],
        num_rows: 82783
    })
})

Review an example i with:

dataset["train"][i]["set"]

Data Instances

More Information Needed

Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

The annotations in this dataset along with this website belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License

Citation Information

More Information Needed

Contributions

Thanks to:

• Tsung-Yi Lin - Google Brain
• Genevieve Patterson - MSR, Trash TV
• Matteo R. - Ronchi Caltech
• Yin Cui - Google
• Michael Maire - TTI-Chicago
• Serge Belongie - Cornell Tech
• Lubomir Bourdev - WaveOne, Inc.
• Ross Girshick - FAIR
• James Hays - Georgia Tech
• Pietro Perona - Caltech
• Deva Ramanan - CMU
• Larry Zitnick - FAIR
• Piotr Dollár - FAIR

for adding this dataset.