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Model: ipst/Qwen2.5-7B-Instruct-SLDS
Source: Original Platform

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README.md

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+---
+base_model: unsloth/Qwen2.5-7B-Instruct
+language:
+- de
+- fr
+- it
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+datasets:
+- ipst/slds
+metrics:
+- bertscore
+- bleu
+- rouge
+---
+# Model Card for Qwen2.5-7B-Instruct-SLDS
+
+## Model Summary
+
+This model is a **Qwen2.5-7B-Instruct fine-tuned on the Swiss Landmark Decisions Summarization (SLDS) dataset**.  
+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.  
+
+The model is optimized for **legal abstractive summarization** and is capable of producing **concise, legally structured headnotes**.  
+It can be used for both **monolingual** and **cross-lingual summarization** tasks.
+
+This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+---
+
+## Intended Use
+
+- **Primary Task**: Judicial summarization (decision → headnote generation).  
+- **Languages**: German (`de`), French (`fr`), Italian (`it`).  
+- **Scenarios**:  
+  - Monolingual summarization: e.g., German decision → German headnote.  
+  - Cross-lingual summarization: e.g., German decision → French headnote.  
+  - Legal research support: assisting in retrieval and navigation of court decisions.  
+
+**Not intended for**:  
+- Replacing human legal expertise.  
+- Serving as an authoritative legal source.  
+- Automated legal advice or decision-making.
+
+---
+
+## Training Data
+
+- **Dataset**: [Swiss Landmark Decisions Summarization (SLDS)](https://huggingface.co/datasets/ipst/slds).  
+- **Size**: ~20K decisions, ~60K decision–headnote pairs.  
+- **Splits**: Train (1954–2021), Validation (2022), Test (2023–2024).  
+- **Source**: [Swiss Federal Supreme Court](https://www.bger.ch).  
+
+---
+
+## Training Procedure
+
+- **Base Models**:  
+  - Qwen2.5 family (0.5B–14B)  
+  - Llama 3.2 (3B)  
+  - Phi-3.5-mini  
+
+- **Fine-tuning Objective**: Conditional generation (decision → headnote).  
+- **Evaluation Metrics**:  
+  - Lexical: ROUGE-1/2/L, BLEU, BERTScore.  
+  - Domain-specific: LLM-as-a-Judge framework (DeepSeek V3) assessing five rubrics: accuracy, completeness, clarity, legal citations, and considerations.  
+
+---
+
+## Model Performance
+
+On the SLDS test set (2023–2024):
+
+| Model | Setting | BERTScore ↑ | BLEU ↑ | ROUGE-1 ↑ | ROUGE-2 ↑ | ROUGE-L ↑ | JUDGE ↑ |
+|:--- |:--- |:--- |:--- |:--- |:--- |:--- |:--- |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| 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 |
+| 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 |
+| 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** |
+| 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 |
+
+- **Lexical metrics**: Fine-tuned models outperform in overlap-based scores.  
+- **LLM-judge scores**: Larger proprietary and reasoning models outperform in legal precision.  
+
+---
+
+## Limitations
+
+- **Language imbalance**: German decisions dominate, while Italian remains underrepresented.  
+- **Biases**: Headnotes reflect judicial style and conventions, not neutral summaries.  
+- **Evaluation mismatch**: ROUGE and BLEU may not fully capture legal accuracy.  
+- **Overfitting risk**: Models may overfit to formulaic headnote structures.  
+- **Cross-lingual difficulty**: Some models struggle with non-monolingual headnote generation.  
+
+---
+
+## Ethical Considerations
+
+- **Sensitive information**: All data is anonymized by the Swiss Federal Supreme Court before publication.  
+- **Legal risk**: Generated headnotes must not be used as official legal advice.  
+- **Fair use**: Ensure attribution when reusing outputs.  
+
+---
+
+## How to Cite
+
+If you use this model, please cite the dataset paper:
+
+```bibtex
+@inproceedings{rolshoven-etal-2025-unlocking,
+    title = "Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in {S}witzerland",
+    author = {Rolshoven, Luca  and
+      Rasiah, Vishvaksenan  and
+      Bose, Srinanda Br{\"u}gger  and
+      Hostettler, Sarah  and
+      Burkhalter, Lara  and
+      St{\"u}rmer, Matthias  and
+      Niklaus, Joel},
+    editor = "Christodoulopoulos, Christos  and
+      Chakraborty, Tanmoy  and
+      Rose, Carolyn  and
+      Peng, Violet",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.findings-emnlp.832/",
+    pages = "15382--15411",
+    ISBN = "979-8-89176-335-7",
+}
+```

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config.json

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+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|PAD_TOKEN|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fab42efe8d17406525a9154b728cf9e957629a8ed7ce997770efdd71128c6a1a
+size 11422086

tokenizer_config.json

@@ -0,0 +1,217 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "single_word": false,
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+    },
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "rstrip": false,
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+      "special": true
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+      "special": true
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+      "special": true
+    },
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+      "special": true
+    },
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+    },
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+      "normalized": false,
+      "rstrip": false,
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+      "special": true
+    },
+    "151657": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<|PAD_TOKEN|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}