|
--- |
|
dataset_info: |
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features: |
|
- name: sentence1 |
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dtype: image |
|
- name: sentence2 |
|
dtype: image |
|
- name: score |
|
dtype: float64 |
|
splits: |
|
- name: test |
|
num_bytes: 33542155.0 |
|
num_examples: 3000 |
|
download_size: 25489936 |
|
dataset_size: 33542155.0 |
|
configs: |
|
- config_name: default |
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data_files: |
|
- split: test |
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path: data/test-* |
|
--- |
|
### Dataset Summary |
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|
|
This dataset is rendered to images from STS-15. We envision the need to assess vision encoders' abilities to understand texts. A natural way will be assessing them with the STS protocols, with texts rendered into images. |
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|
|
**Examples of Use** |
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|
|
Load test split: |
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|
|
```python |
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from datasets import load_dataset |
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dataset = load_dataset("Pixel-Linguist/rendered-sts15", split="test") |
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``` |
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|
|
### Languages |
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|
|
English-only; for multilingual and cross-lingual datasets, see `Pixel-Linguist/rendered-stsb` and `Pixel-Linguist/rendered-sts17` |
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|
|
### Citation Information |
|
|
|
``` |
|
@article{xiao2024pixel, |
|
title={Pixel Sentence Representation Learning}, |
|
author={Xiao, Chenghao and Huang, Zhuoxu and Chen, Danlu and Hudson, G Thomas and Li, Yizhi and Duan, Haoran and Lin, Chenghua and Fu, Jie and Han, Jungong and Moubayed, Noura Al}, |
|
journal={arXiv preprint arXiv:2402.08183}, |
|
year={2024} |
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} |
|
``` |
