Welcome to our comprehensive guide on using an AI model fine-tuned for generating legal document drafts, specifically focused on fraud and theft cases. This user-friendly article will help you navigate the intricacies of this powerful tool, troubleshoot common issues, and understand the underlying processes involved.

Introduction to the AI Model

This AI model leverages the BLOOM 560m architecture, specially fine-tuned with a dataset containing legal verdicts related to fraud cases. From January 1, 2011, to December 31, 2021, the model was trained using 74,823 verdicts, allowing it to generate coherent and contextually relevant drafts for legal documents.

Getting Started

To utilize this model in your applications, you have two primary methods: calling the API or downloading it locally. Below are guides for both approaches.

Using the API

Start by setting up the necessary API token and sending requests to the inference server.

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" # Your API token here

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(query_dict["text_len"], desc="Generating example draft", 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"fi: {response[0]['generated_text']}")
        t.refresh()
    except KeyError:
        sleep(30) # If the server is too busy, wait 30 seconds and retry.
        pass

print(response[0]["generated_text"])

Using the Transformers Library

If you prefer to work locally, you can use the Transformers library to deploy the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

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

Understanding the Code with an Analogy

Think of the model as a chef in a restaurant. The data it was trained on represents the chef’s recipes — in this case, legal verdict drafts. When you request a dish (draft), the chef will follow one of the recipes to prepare it, ensuring that the result is flavorful (coherent and contextually relevant). The API acts like a waiter, taking your order and delivering it to the chef, while the responses are the meals served to you.

Troubleshooting Common Issues

• Model Not Responding: If the server appears busy, you may need to implement a delay (as shown with the sleep function) before trying again.
• Invalid API Token: Double-check that your API token is correctly entered and is active.
• Unexpected Output: Ensure that your input prompt is clear and well-structured to achieve better results.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

This guide serves as an introductory exploration into the world of AI-assisted legal document drafting, outlining methods to implement and troubleshoot the technology. By employing this AI model, legal professionals can efficiently generate drafts for fraud and theft cases.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.