Update README.md

README.md

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 ---
 license: mit
+language:
+- en
 ---
+
+# STRONG Model Card
+
+## Model Information
+
+Summary description and brief definition of inputs and outputs.
+
+### Description
+
+STRONG is a finetuned LED-based model that can produce structure controllable summarization of long legal opinions obtained from CanLII.
+
+You can also find the fine-tuned model without structure information [here](https://huggingface.co/yznlp/STRONG-LED-NoStructure).
+
+### Usage
+
+Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
+
+The input is composed of two parts:
+1. Summary Structure Prompt: Concatenate a series of IRC structure labels using " | " as a separator. (labels include Non_IRC, Issue, Reason, Conclusion).
+2. After the special token " ==> ", enter the text of the legal opinion.
+
+#### Running the model on a CPU
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384")
+model = AutoModelForCausalLM.from_pretrained("yznlp/STRONG-LED")
+
+input_text = "Non_IRC | Issue | Conclusion ==> {Legal Case Content}"
+input_ids = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**input_ids, max_length=256, num_beams=4, length_penalty=2.0)
+print(tokenizer.decode(outputs[0]))
+```
+
+
+#### Running the model on a single / multi GPU
+
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384")
+model = AutoModelForCausalLM.from_pretrained("yznlp/STRONG-LED", device_map="auto")
+
+input_text = "Non_IRC | Issue | Conclusion ==> {Legal Case Content}"
+input_ids = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**input_ids, max_length=256, num_beams=4, length_penalty=2.0)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Paper Citation
+If you find our model useful, please cite
+```
+@inproceedings{zhong-litman-2023-strong,
+    title = "{STRONG} {--} Structure Controllable Legal Opinion Summary Generation",
+    author = "Zhong, Yang  and
+      Litman, Diane",
+    editor = "Park, Jong C.  and
+      Arase, Yuki  and
+      Hu, Baotian  and
+      Lu, Wei  and
+      Wijaya, Derry  and
+      Purwarianti, Ayu  and
+      Krisnadhi, Adila Alfa",
+    booktitle = "Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)",
+    month = nov,
+    year = "2023",
+    address = "Nusa Dua, Bali",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.findings-ijcnlp.37",
+    pages = "431--448",
+}
+```