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+# PixT3: Pixel-based Table-To-Text Generation
+This repository contains code and datasets for the ACL 2024 paper [PixT3: Pixel-based Table-To-Text Generation](https://aclanthology.org/2024.acl-long.364/).
+
+We release PixT3 model checkpoints for the TControl, LControl, and OpenE settings as well as 
+ToTTo, Controlled Logic2Text, and SLC pretraining datasets alongside their corresponding rendered tables
+for each setting. This repository also contains the code to train and evaluate these models.
+
+## Datasets
+Download the ready-to-use datasets in _Files and versions_.
+
+## Model checkpoints
+Download model checkpoints in _Files and versions_.
+
+Model names:
+- **PixT3 (TControl):** `pixt3_tcontrol`
+- **PixT3 (LControl):** `pixt3_lcontrol`
+- **PixT3 (OpenE):** `pixt3_opene`
+- **PixT3 (SLC):** `pixt3_slc` This is the model pretrained with the Structure Learning Curriculum. It mainly serves as initialization checkpoint for PixT3 (LControl) and PixT3 (OpenE).
+
+## Reference
+If you find this project useful, please cite it using the following format
+
+```
+@inproceedings{alonso-etal-2024-pixt3,
+    title = "{P}ix{T}3: Pixel-based Table-To-Text Generation",
+    author = "Alonso, I{\~n}igo  and
+      Agirre, Eneko  and
+      Lapata, Mirella",
+    editor = "Ku, Lun-Wei  and
+      Martins, Andre  and
+      Srikumar, Vivek",
+    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.acl-long.364",
+    pages = "6721--6736",
+}
+```