|
# Altogether-FT |
|
|
|
(EMNLP 2024) Altogether-FT is an annotated fine-tuning dataset that re-aligns alt-texts into dense captions. It powers altogether captioner to transform Internet-scale quality alt-texts into dense captions, instead of captioning from scratch as naive captions (e.g, "a dog is walking in the park."). |
|
It contains 15448 examples for training and 500 examples for evaluation from WIT and DataComp. |
|
|
|
 |
|
|
|
|
|
```bibtex |
|
@inproceedings{xu2024altogether, |
|
title={Altogether: Image Captioning via Re-aligning Alt-text}, |
|
author={Hu Xu, Po-Yao Huang, Xiaoqing Ellen Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie and Christoph Feichtenhofer}, |
|
journal={arXiv preprint arXiv:xxxx.xxxxx}, |
|
year={2024} |
|
} |
|
``` |
|
|
|
## Altogether-FT |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
train_dataset = load_dataset("json", data_files="activebus/Altogether-FT/altogether_ft_train.json", field="data") |
|
|
|
eval_dataset = load_dataset("json", data_files="activebus/Altogether-FT/altogether_ft_eval.json", field="data") |
|
``` |
|
|
|
|
|
## License |
|
|
|
The majority of Altogether-FT is licensed under CC-BY-NC, portions of the project are available under separate license terms: CLIPCap is licensed MIT and open_clip is licensed under the https://github.com/mlfoundations/open_clip license. |
|
|
|
|
|
--- |
|
license: cc-by-nc-4.0 |
|
--- |
|
|