Update README.md

README.md

@@ -1,8 +1,63 @@
+---
+language: en
+tags:
+- AMRBART
+license: mit
+---
+
 ## AMRBART (large-sized model)
 
-AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. and first released in [this repository](https://github.com/muyeby/AMRBART). 
+AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository](https://github.com/muyeby/AMRBART). 
 
+## Model description
 
----
-license: mit
----
+AMRBART follows the BART model which uses a transformer encoder-encoder architecture. AMRBART is pre-trained with 6 tasks:
+
++ learning to reconstruct the text based on the corrupted text.
++ learning to reconstruct AMR graphs based on the corrupted AMR graph.
++ learning to reconstruct the text based on the corrupted text and its corresponding AMR graph.
++ learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding text.
++ learning to reconstruct the text based on the corrupted text and its corresponding corrupted AMR graph.
++ learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding corrupted text.
+
+AMRBART is particularly effective when fine-tuned for AMR parsing and AMR-to-text generation tasks.
+
+## Training data
+
+The AMRBART model is pre-trained on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635
+training instances and [English Gigaword](https://catalog.ldc.upenn.edu/LDC2003T05) (we randomly sampled 200,000 sentences).
+
+## Intended uses & limitations
+
+You can use the raw model for either AMR encoding or AMR parsing, but it's mostly intended to
+be fine-tuned on a downstream task. 
+
+## How to use
+Here is how to initialize this model in PyTorch:
+
+```python
+from transformers import BartModel
+model = BartModel.from_pretrained("xfbai/AMRBART-large")
+```
+Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preprocessing.
+
+
+## BibTeX entry and citation info
+Please cite this paper if you find this model helpful
+
+```bibtex
+@inproceedings{bai-etal-2022-graph,
+    title = "Graph Pre-training for {AMR} Parsing and Generation",
+    author = "Bai, Xuefeng  and
+      Chen, Yulong and
+      Zhang, Yue",
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = may,
+    year = "2022",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "todo",
+    doi = "todo",
+    pages = "todo"
+}
+```