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