Update README.md

README.md

@@ -1,3 +1,36 @@
----
-license: cc-by-nc-4.0
----
+# 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.
+
+![Altogether](altogether.png)
+
+
+```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
+---