Model Card for Model ID
Model Details
Model Description
DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
- Developed by: [Ali Asghar (jaffry258@gmail.com)]
- Funded by [optional]: [still under progress ]
- Shared by [optional]: []
- Model type: [Large Language Model (LLM)]
- Language(s) (NLP): [pytorch,transformers,python]
- License: [Appache 2.0]
- Finetuned from model [optional]: [Qwen2.5-3B]
Model Sources [optional]
- Repository: [https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB/tree/main]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
Direct Use
Legal research: Extract, summarize, and analyze BGB texts.
AI-powered legal assistants: Provide insights into German law.
Academic purposes: Assists in legal document structuring.
[More Information Needed]
Downstream Use [optional]
Chatbots for legal guidance.
AI-based contract analysis.
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
The model may reflect biases in the BGB dataset.
Not suitable for real-time legal decision-making.
Might struggle with non-German legal texts.
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
- trainer = GRPOTrainer(
model = model,
processing_class = tokenizer,
reward_funcs = [
xmlcount_reward_func,
soft_format_reward_func,
strict_format_reward_func,
int_reward_func,
correctness_reward_func,
],
args = training_args,
train_dataset = dataset,
)
trainer.train()
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
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Metrics
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Results
[More Information Needed]
Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
@article{DeutscheLexAI_BGB,
title={DeutscheLexAI_BGB: A Fine-Tuned Qwen2.5-3B Model for German Legal Texts},
author={Your Name or Organization},
journal={Hugging Face Model Hub},
year={2025},
url={
https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB}
}
BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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