Updated model card.

README.md

@@ -1,12 +1,13 @@
 ---
 library_name: transformers
-tags: []
+base_model:
+- google-t5/t5-base
 ---
 
 # Model Card for STEP
 
 <!-- Provide a quick summary of what the model is/does. -->
-This model is pre-trained to perform (random) syntactic transformations of English sentences. The prefix given to the model decides, which syntactic transformation to apply. 
+This model is pre-trained to perform (random) syntactic transformations of English sentences. The prefix given to the model decides which syntactic transformation to apply. 
 
 See [Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations](https://arxiv.org/abs/2407.04543) for full details.
 
@@ -34,7 +35,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## Uses
 
-Syntax-sensitive sequence-to-sequence for English such as passivization, semantic parsing, question formation, ...
+Syntax-sensitive sequence-to-sequence for English such as passivization, semantic parsing, chunking, question formation, ...
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 ### Direct Use
@@ -92,6 +93,8 @@ We identified the following interpretable transformation look-up heads (see pape
 
 - **Hardware Type:** Nvidia 2080 TI
 - **Hours used:** 30
+- **Compute Regsion**: Scotland
+- **Carbon Emitted**: 0.2 kg CO2eq
 
 ## Technical Specifications
 
@@ -105,13 +108,17 @@ T5-Base, 12 layers, hidden dimensionality of 768.
 
 **BibTeX:**
 ```
-@misc{lindemann2024strengtheningstructuralinductivebiases,
-      title={Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations}, 
-      author={Matthias Lindemann and Alexander Koller and Ivan Titov},
-      year={2024},
-      eprint={2407.04543},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2407.04543}, 
+@inproceedings{lindemann-etal-2024-strengthening,
+    title = "Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations",
+    author = "Lindemann, Matthias  and
+      Koller, Alexander  and
+      Titov, Ivan",
+    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.emnlp-main.645/",
+    doi = "10.18653/v1/2024.emnlp-main.645",
 }
-```
+```