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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: filepath |
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dtype: string |
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- name: sentids |
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list: int32 |
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- name: filename |
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dtype: string |
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- name: imgid |
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dtype: int32 |
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- name: split |
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dtype: string |
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- name: sentences_tokens |
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list: |
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list: string |
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- name: sentences_raw |
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list: string |
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- name: sentences_sentid |
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list: int32 |
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- name: cocoid |
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dtype: int32 |
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- name: th_sentences_raw |
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sequence: string |
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splits: |
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- name: test |
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num_bytes: 819234726.0 |
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num_examples: 5000 |
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- name: validation |
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num_bytes: 807387321.0 |
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num_examples: 5000 |
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- name: train |
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num_bytes: 18882795327.165 |
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num_examples: 113287 |
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download_size: 20158273111 |
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dataset_size: 20509417374.165 |
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--- |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("patomp/thai-mscoco-2014-captions") |
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dataset |
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``` |
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output |
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```python |
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DatasetDict({ |
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train: Dataset({ |
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features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'], |
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num_rows: 113287 |
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}) |
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validation: Dataset({ |
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features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'], |
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num_rows: 5000 |
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}) |
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test: Dataset({ |
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features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'], |
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num_rows: 5000 |
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}) |
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}) |
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``` |
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A sample |
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```python |
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dataset["validation"][0] |
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``` |
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output |
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```python |
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{ |
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"image":<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x7F6C5A83F430>, |
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"filepath":"COCO_val2014_000000184613.jpg", |
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"sentids":[474921,479322,479334,481560,483594], |
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"filename":"COCO_val2014_000000184613.jpg", |
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"imgid":2, |
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"split":"val", |
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"sentences_tokens":[ |
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["a", "child","holding", "a","flowered","umbrella","and","petting","a","yak"],["a","young","man","holding","an","umbrella","next","to","a","herd","of","cattle"], |
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["a","young","boy","barefoot","holding","an","umbrella","touching","the","horn","of","a","cow"], |
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["a","young","boy","with","an","umbrella","who","is","touching","the","horn","of","a","cow"], |
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["a","boy","holding","an","umbrella","while","standing","next","to","livestock"] |
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], |
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"sentences_raw":[ |
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"A child holding a flowered umbrella and petting a yak.", |
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"A young man holding an umbrella next to a herd of cattle.", |
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"a young boy barefoot holding an umbrella touching the horn of a cow", |
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"A young boy with an umbrella who is touching the horn of a cow.", |
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"A boy holding an umbrella while standing next to livestock." |
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], |
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"sentences_sentid":[474921,479322,479334,481560,483594], |
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"cocoid":184613, |
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"th_sentences_raw":[ |
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"เด็กถือร่มที่มีดอกหนึ่งคันและลูบคลูบลํา", |
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"ชายหนุ่มคนหนึ่งถือร่มไว้ข้างๆ ฝูงวัว", |
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"เด็กหนุ่มคนหนึ่งเท้าเปล่าจับร่มจับแตรของวัว", |
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"เด็กชายที่มีร่มสัมผัสแตรของวัว", |
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"เด็กชายถือร่มในขณะที่ยืนถัดจากปศุสัตว์" |
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] |
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} |
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``` |
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## Dataset Construction |
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The dataset contructed from translating the captions of [MS COCO 2014 dataset](https://huggingface.co/datasets/HuggingFaceM4/COCO) [1] to Thai by using [NMT](https://airesearch.in.th/releases/machine-translation-models/) provided by VISTEC-depa Thailand Artificial Intelligence Research Institute [2]. The translated of 3 splits (train, validation and test) dataset was published in the [Huggingface](https://huggingface.co/datasets/patomp/thai-mscoco-2014-captions). |
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## References |
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[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context. In Computer Vision – ECCV 2014, Springer International Publishing, Cham, 740–755. |
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[2] English-Thai Machine Translation Models. (2020, June 23). VISTEC-depa Thailand Artificial Intelligence Research Institute. https://airesearch.in.th/releases/machine-translation-models/ |