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判決書草稿自動生成

本模型是以司法院公開之「詐欺」案件判決書做成之資料集,基於 BLOOM 1b1 模型進行微調,可以自動生成詐欺及竊盜案件之犯罪事實段落之草稿。資料集之資料範圍從100年1月1日至110年12月31日,所蒐集到的原始資料共有 74823 篇(判決以及裁定),我們只取判決書的「犯罪事實」欄位內容,並把這原始的資料分成三份,用於訓練的資料集有59858篇,約佔原始資料的80%,剩下的20%,則是各分配10%給驗證集(7482篇),10%給測試集(7483篇)。在本網頁進行測試時,請在模型載入完畢並生成第一小句後,持續按下Compute按鈕,就能持續生成文字。或是輸入自己想要測試的資料到文字框中進行測試。

使用範例

如果要在自己的程式中調用本模型,可以參考下列的 Python 程式碼,或許可以藉由呼叫 API 的方式來生成刑事判決書「犯罪事實」欄的內容。(本模型有點大,可能無法呼叫成功。)

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import requests, json
from time import sleep
from tqdm.auto import tqdm, trange

API_URL = "https://api-inference.huggingface.co/models/jslin09/bloom-1b1-finetuned-fraud" API_TOKEN = 'XXXXXXXXXXXXXXX' # 調用模型的 API token headers = {"Authorization": f"Bearer {API_TOKEN}"}

def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return json.loads(response.content.decode("utf-8"))

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'])

或是,你要使用 transformers 套件來實作你的程式,將本模型下載至你本地端的電腦中執行,可以參考下列程式碼:

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from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jslin09/bloom-1b1-finetuned-fraud") model = AutoModelForCausalLM.from_pretrained("jslin09/bloom-1b1-finetuned-fraud")

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Dataset used to train jslin09/bloom-1b1-finetuned-fraud