Alijeff1214/DeutscheLexAI_BGB_2.0

Model Card for Model ID

Model Details

Model Description

DeutscheLexAI_BGB_2,0 is a fine-tuned Qwen2.5-3B model with more training and accurate version with output context length upto 500 tokens 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 ([email protected])]
• 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_2.0/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

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Training Procedure

Preprocessing [optional]

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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()

Test on HF Space

https://huggingface.co/spaces/Alijeff1214/DeutecheLexAI_BGB

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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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

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Compute Infrastructure

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Hardware

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Software

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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_2.0} }

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]

Ali Asghar

Model Card Contact

[email protected]