Update README.md

README.md

@@ -1,3 +1,52 @@
 ---
+language: en
+tags:
+- AMRBART
 license: mit
 ---
+
+## AMRBART-large-finetuned-AMR3.0-AMRParsing
+
+This model is a fine-tuned version of [AMRBART-large](https://huggingface.co/xfbai/AMRBART-large) on an AMR3.0 dataset. It achieves a Smatch of 84.2 on the evaluation set: More details are 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.
+
+## Model description
+Same with AMRBART.
+
+## Training data
+
+The model is finetuned on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635
+training instances, 1722 validation instances, and 1898 test instances.
+
+## Intended uses & limitations
+
+You can use the model for AMR parsing, but it's mostly intended to be used in the domain of News. 
+
+## How to use
+Here is how to initialize this model in PyTorch:
+
+```python
+from transformers import BartForConditionalGeneration
+model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large-finetuned-AMR3.0-AMRParsing")
+```
+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"
+}
+```