• Model creator: https://huggingface.co/jslin09/
• Original model: https://huggingface.co/jslin09/bloom-560m-finetuned-fraud/

Original model description:

• jslin09/Fraud_Case_Verdicts language:
• zh metrics:
• accuracy pipeline_tag: text-generation text-generation: parameters: max_length: 400 max_new_tokens: 400 do_sample: true temperature: 0.75 top_k: 50 top_p: 0.9 tags:
• legal widget:
• text: 王大明意圖為自己不法所有，基於竊盜之犯意， example_title: 生成竊盜罪之犯罪事實
• text: 騙人布意圖為自己不法所有，基於詐欺取財之犯意， example_title: 生成詐欺罪之犯罪事實
• text: 梅友乾明知其無資力支付酒店消費，亦無付款意願，竟意圖為自己不法之所有， example_title: 生成吃霸王餐之詐欺犯罪事實
• text: 闕很大明知金融帳戶之存摺、提款卡及密碼係供自己使用之重要理財工具， example_title: 生成賣帳戶幫助詐欺犯罪事實
• text: 通訊王明知近來盛行以虛設、租賃、借用或買賣行動電話人頭門號之方式，供詐騙集團作為詐欺他人交付財物等不法用途， example_title: 生成賣電話SIM卡之幫助詐欺犯罪事實
• text: 趙甲王基於行使偽造特種文書及詐欺取財之犯意， example_title: 偽造特種文書(契約、車牌等)詐財

判決書「犯罪事實」欄草稿自動生成

使用範例

import requests, json
from time import sleep
from tqdm.auto import tqdm, trange
[object Object]
[object Object]

prompt = "森上梅前明知其無資力支付酒店消費，亦無付款意願，竟意圖為自己不法之所有，"
query_dict = {
"inputs": prompt,
}
text_len = 300
t = trange(text_len, desc= '生成例稿', leave=True)
for i in t:
response = query(query_dict)
try:
response_text = response[0]['generated_text']
query_dict["inputs"] = response_text
t.set_description(f"{i}: {response[0]['generated_text']}")
t.refresh()
except KeyError:
sleep(30) # 如果伺服器太忙無回應，等30秒後再試。
pass
print(response[0]['generated_text'])

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jslin09/bloom-560m-finetuned-fraud")
model = AutoModelForCausalLM.from_pretrained("jslin09/bloom-560m-finetuned-fraud")

本模型進行各項指標進行評估的結果如下 Open LLM Leaderboard Evaluation Results

引文訊息

@misc{lin2024legal,
      title={Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language Model}, 
      author={Chun-Hsien Lin and Pu-Jen Cheng},
      year={2024},
      eprint={2406.04202},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
      url = {https://arxiv.org/abs/2406.04202}
}