- official link (korean): https://dacon.io/competitions/official/236112/overview/description
- This challenge aims to develop an AI that predicts legal case outcomes. The significance is a crucial step in exploring how AI can be effectively utilized in the field of law.
-
The final score is 0.57258 (i.e., 1st place among 837 people).
-
Overview
- Install libraries for text classification models.
python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt- Preprocess all training and testing samples.
python3 preprocess.py
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_trainval.py --model google/bigbird-pegasus-large-bigpatent --tag bigbird-pegasus-large-bigpatent
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_trainval.py --model google/rembert --tag rembert
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_trainval.py --model microsoft/deberta-v2-xxlarge --tag deberta-v2-xxlarge
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_trainval.py --model albert-xxlarge-v2 --tag albert-xxlarge-v2
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_test.py --model google/bigbird-pegasus-large-bigpatent --tag bigbird-pegasus-large-bigpatent
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_test.py --model google/rembert --tag rembert
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_test.py --model microsoft/deberta-v2-xxlarge --tag deberta-v2-xxlarge
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_test.py --model albert-xxlarge-v2 --tag albert-xxlarge-v2
CUDA_VISIBLE_DEVICES=0 python3 extract_embs_for_llm.py --file ./open/train.json
CUDA_VISIBLE_DEVICES=1 python3 extract_embs_for_llm.py --file ./open/test.json
python3 generate_qa_list_for_llm.py- Run text classification models (i.e., RemBeRT, ALBERT, DeBERTa, and BigBirdPegasus)
- Please download pretrained weights following this link.
CUDA_VISIBLE_DEVICES=0 python3 infer_classification_models.py \
--model_names rembert,albert-xxlarge-v2,deberta-v2-xxlarge,bigbird-pegasus-large-bigpatent- Install libraries for large langage models (i.e., vicuna).
deactivate
cd llm
python3 -m venv venv
source ./venv/bin/activate
pip3 install --upgrade pip
pip3 install -e .
git lfs install
git clone https://huggingface.co/lmsys/vicuna-13b-v1.3- Run vicuna-13b-v1.3 using the dataset for few-shot learning.
python3 -m fastchat.serve.controller
python3 -m fastchat.serve.model_worker --model-path vicuna-13b-v1.3 --port 21002
python3 -m run_llm \
--controller-address "http://localhost:21001" --model-name vicuna-13b-v1.3 \
--temperature 0.001 --max-new-tokens 100
- Produce the final result by unifying two results from classification and language models.
python3 ensemble_all_results.py- Train four classification models.
CUDA_VISIBLE_DEVICES=0 python3 train.py --model bigbird-pegasus-large-bigpatent
CUDA_VISIBLE_DEVICES=0 python3 train.py --model rembert
CUDA_VISIBLE_DEVICES=0 python3 train.py --model deberta-v2-xxlarge
CUDA_VISIBLE_DEVICES=0 python3 train.py --model albert-xxlarge-v2- Thanks to the authors of Vicuna-13B-v1.3 used in this respository.
If you have any question or find any bug, please email me.
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