144 lines
6.2 KiB
Markdown
144 lines
6.2 KiB
Markdown
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---
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base_model: unsloth/Qwen2.5-7B-Instruct
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language:
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- de
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- fr
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- it
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- trl
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datasets:
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- ipst/slds
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metrics:
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- bertscore
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- bleu
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- rouge
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---
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# Model Card for Qwen2.5-7B-Instruct-SLDS
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## Model Summary
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This model is a **Qwen2.5-7B-Instruct fine-tuned on the Swiss Landmark Decisions Summarization (SLDS) dataset**.
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SLDS is a multilingual dataset of **20,000 Swiss Federal Supreme Court decisions** (1954–2024), each paired with **headnotes in German, French, and Italian**, resulting in ~60,000 decision–headnote pairs.
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The model is optimized for **legal abstractive summarization** and is capable of producing **concise, legally structured headnotes**.
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It can be used for both **monolingual** and **cross-lingual summarization** tasks.
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This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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---
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## Intended Use
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- **Primary Task**: Judicial summarization (decision → headnote generation).
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- **Languages**: German (`de`), French (`fr`), Italian (`it`).
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- **Scenarios**:
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- Monolingual summarization: e.g., German decision → German headnote.
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- Cross-lingual summarization: e.g., German decision → French headnote.
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- Legal research support: assisting in retrieval and navigation of court decisions.
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**Not intended for**:
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- Replacing human legal expertise.
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- Serving as an authoritative legal source.
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- Automated legal advice or decision-making.
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---
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## Training Data
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- **Dataset**: [Swiss Landmark Decisions Summarization (SLDS)](https://huggingface.co/datasets/ipst/slds).
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- **Size**: ~20K decisions, ~60K decision–headnote pairs.
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- **Splits**: Train (1954–2021), Validation (2022), Test (2023–2024).
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- **Source**: [Swiss Federal Supreme Court](https://www.bger.ch).
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---
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## Training Procedure
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- **Base Models**:
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- Qwen2.5 family (0.5B–14B)
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- Llama 3.2 (3B)
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- Phi-3.5-mini
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- **Fine-tuning Objective**: Conditional generation (decision → headnote).
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- **Evaluation Metrics**:
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- Lexical: ROUGE-1/2/L, BLEU, BERTScore.
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- Domain-specific: LLM-as-a-Judge framework (DeepSeek V3) assessing five rubrics: accuracy, completeness, clarity, legal citations, and considerations.
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---
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## Model Performance
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On the SLDS test set (2023–2024):
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| Model | Setting | BERTScore ↑ | BLEU ↑ | ROUGE-1 ↑ | ROUGE-2 ↑ | ROUGE-L ↑ | JUDGE ↑ |
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|:--- |:--- |:--- |:--- |:--- |:--- |:--- |:--- |
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| [Phi-3.5-mini](https://huggingface.co/ipst/Phi-3.5-mini-instruct-SLDS) | fine-tuned | 11.24 ± 3.82 | 34.84 ± 0.41 | 31.20 ± 2.08 | 14.11 ± 1.27 | 20.96 ± 1.35 | 15.25 ± 2.32 |
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| [Llama 3.2B](https://huggingface.co/ipst/Llama-3.2-3B-Instruct-SLDS) | fine-tuned | 15.20 ± 4.40 | 21.89 ± 0.42 | 31.89 ± 2.34 | 14.87 ± 1.61 | 22.49 ± 1.60 | 18.47 ± 2.99 |
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| [Qwen2.5 0.5B](https://huggingface.co/ipst/Qwen2.5-0.5B-Instruct-SLDS) | fine-tuned | -1.37 ± 3.85 | 32.20 ± 0.35 | 23.87 ± 1.68 | 9.46 ± 0.94 | 17.37 ± 1.09 | 5.80 ± 1.26 |
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| [Qwen2.5 1.5B](https://huggingface.co/ipst/Qwen2.5-1.5B-Instruct-SLDS) | fine-tuned | 19.81 ± 2.72 | 36.79 ± 0.34 | 33.03 ± 1.73 | 14.14 ± 1.08 | 22.67 ± 1.13 | 15.92 ± 2.27 |
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| [Qwen2.5 3B](https://huggingface.co/ipst/Qwen2.5-3B-Instruct-SLDS) | fine-tuned | 23.23 ± 2.80 | 38.42 ± 0.34 | 35.18 ± 1.79 | 15.66 ± 1.23 | 24.10 ± 1.17 | 20.31 ± 2.66 |
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| [Qwen2.5 7B](https://huggingface.co/ipst/Qwen2.5-7B-Instruct-SLDS) | fine-tuned | 29.59 ± 1.97 | 41.40 ± 0.34 | 39.24 ± 1.59 | 18.26 ± 1.25 | 26.44 ± 1.15 | 28.37 ± 3.07 |
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| [Qwen2.5 14B](https://huggingface.co/ipst/Qwen2.5-14B-Instruct-SLDS) | fine-tuned | **32.48 ± 1.98** | **41.80 ± 0.37** | 40.04 ± 1.74 | **19.99 ± 1.41** | **28.00 ± 1.28** | 31.38 ± 3.19 |
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| GPT-4o | one-shot | 30.44 ± 1.74 | 31.89 ± 0.25 | **42.12 ± 1.79** | 18.92 ± 1.22 | 25.92 ± 1.05 | 39.70 ± 2.66 |
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| Claude 3.5 Sonnet | one-shot | 5.53 ± 2.00 | 21.88 ± 0.25 | 41.86 ± 1.64 | 19.23 ± 1.19 | 27.67 ± 1.20 | 41.25 ± 2.90 |
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| DeepSeek-R1 | one-shot | 20.28 ± 1.45 | 22.37 ± 0.18 | 38.30 ± 1.82 | 15.97 ± 0.85 | 21.03 ± 0.84 | **42.28 ± 2.21** |
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| o3-mini | one-shot | 14.18 ± 1.31 | 20.55 ± 0.17 | 34.77 ± 1.43 | 11.92 ± 0.69 | 18.21 ± 0.67 | 34.82 ± 2.41 |
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- **Lexical metrics**: Fine-tuned models outperform in overlap-based scores.
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- **LLM-judge scores**: Larger proprietary and reasoning models outperform in legal precision.
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---
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## Limitations
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- **Language imbalance**: German decisions dominate, while Italian remains underrepresented.
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- **Biases**: Headnotes reflect judicial style and conventions, not neutral summaries.
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- **Evaluation mismatch**: ROUGE and BLEU may not fully capture legal accuracy.
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- **Overfitting risk**: Models may overfit to formulaic headnote structures.
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- **Cross-lingual difficulty**: Some models struggle with non-monolingual headnote generation.
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---
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## Ethical Considerations
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- **Sensitive information**: All data is anonymized by the Swiss Federal Supreme Court before publication.
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- **Legal risk**: Generated headnotes must not be used as official legal advice.
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- **Fair use**: Ensure attribution when reusing outputs.
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---
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## How to Cite
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If you use this model, please cite the dataset paper:
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```bibtex
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@inproceedings{rolshoven-etal-2025-unlocking,
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title = "Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in {S}witzerland",
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author = {Rolshoven, Luca and
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Rasiah, Vishvaksenan and
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Bose, Srinanda Br{\"u}gger and
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Hostettler, Sarah and
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Burkhalter, Lara and
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St{\"u}rmer, Matthias and
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Niklaus, Joel},
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editor = "Christodoulopoulos, Christos and
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Chakraborty, Tanmoy and
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Rose, Carolyn and
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Peng, Violet",
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
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month = nov,
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year = "2025",
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address = "Suzhou, China",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2025.findings-emnlp.832/",
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pages = "15382--15411",
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ISBN = "979-8-89176-335-7",
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}
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```
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