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Model: jslin09/bloom-560m-finetuned-fraud Source: Original Platform
2026-05-11 19:31:35 +08:00
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--- a/README.md
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+---
+license: bigscience-bloom-rail-1.0
+datasets:
+- 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
+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 560m](https://huggingface.co/bigscience/bloom-560m) 模型進行微調訓練，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。或是可以到[這裡](https://huggingface.co/spaces/jslin09/legal_document_drafting)有更完整的使用體驗。
+
+# 比較
+以下是本模型與經過微調後的BLOOM 560m、Llama 3.2-1b以 [ROUGE-L](https://en.wikipedia.org/wiki/ROUGE_(metric)) 做評估後的散點圖。
+![ROUGE-L](https://i.imgur.com/V20ER70.png)
+
+# 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，藉由呼叫 API 的方式來生成刑事判決書「犯罪事實」欄的內容。
+<details>
+  <summary> 點擊後展開 </summary>
+<pre>
+  <code>
+import requests, json
+from time import sleep
+from tqdm.auto import tqdm, trange
+
+API_URL = "https://api-inference.huggingface.co/models/jslin09/bloom-560m-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'])
+</code>
+</pre>
+</details>
+
+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:
+<details>
+  <summary> 點擊後展開 </summary>
+<pre>
+  <code>
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("jslin09/bloom-560m-finetuned-fraud")
+model = AutoModelForCausalLM.from_pretrained("jslin09/bloom-560m-finetuned-fraud")
+</code>
+</pre>
+</details>
+
+# 本模型進行各項指標進行評估的結果如下 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+詳細的結果在 [這裡](https://huggingface.co/datasets/open-llm-leaderboard/details_jslin09__bloom-560m-finetuned-fraud)。
+本模型只使用範圍相當小的資料集進行微調，就任務目標來說，已經是完美解決，但就廣泛的通用性來說，其實是不完美的。總的來說，如果應用場景是需要把模型建置在本地端、不能連到外部網路、提示字資料也不能外送的情境下，本模型的建置過程及結果提供了一個可行性的示範。
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 18.37   |
+| ARC (25-shot)         | 26.96          |
+| HellaSwag (10-shot)   | 28.87    |
+| MMLU (5-shot)         | 24.03         |
+| TruthfulQA (0-shot)   | 0.0   |
+| Winogrande (5-shot)   | 48.38   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 0.33         |
+
+# 引文訊息
+
+```
+@article{lin2025assisting,
+  title={Assisting Drafting of Chinese Legal Documents Using Fine-Tuned Pre-trained Large Language Models},
+  author={Lin, Chun-Hsien and Cheng, Pu-Jen},
+  journal={The Review of Socionetwork Strategies},
+  pages={1--28},
+  year={2025},
+  publisher={Springer}
+}
+```
--- a/config.json
+++ b/config.json
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+{
+  "_name_or_path": "./model/bloom-560m-finetuned-fraud",
+  "apply_residual_connection_post_layernorm": false,
+  "architectures": [
+    "BloomForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "attention_softmax_in_fp32": true,
+  "bias_dropout_fusion": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_dropout": 0.0,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "masked_softmax_fusion": true,
+  "model_type": "bloom",
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 24,
+  "offset_alibi": 100,
+  "pad_token_id": 3,
+  "pretraining_tp": 1,
+  "skip_bias_add": true,
+  "skip_bias_add_qkv": false,
+  "slow_but_exact": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "unk_token_id": 0,
+  "use_cache": true,
+  "vocab_size": 250880
+}
--- a/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 3,
+  "transformers_version": "4.26.1"
+}
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+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}
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+  "eos_token": "</s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "name_or_path": "bigscience/bloom-560m",
+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "BloomTokenizer",
+  "unk_token": "<unk>"
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