Hasin2026/bangladesh-scob-judgment-summarization

Bangladesh Supreme Court (SCOB) High Court Division Judgment Summarization Dataset

Dataset Summary

The Bangladesh Supreme Court (SCOB) High Court Division Judgment Summarization Dataset is a curated, high-quality legal NLP dataset comprising all 235 canonical judgments published in the Supreme Court Online Bulletin (SCOB) by the High Court Division of the Supreme Court of Bangladesh.

Each sample pairs a complete, cleaned legal judgment body with its official ground-truth Short Ratio (ratio decidendi / core settled legal principle).

The dataset is provided in two ready-to-use configurations:

1. default: Tabular legal records containing the case citations, metadata, cleaned substantive judgment text, and the target short ratio.
2. sft: Instruction-tuned conversational format (messages schema) optimized for Supervised Fine-Tuning (SFT) of modern LLMs (such as Google Gemma 2, Llama 3, Mistral, and Qwen) using Hugging Face TRL SFTTrainer.

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Dataset Structure & Configurations

Configuration 1: default (Canonical Legal Metadata & Text)

• file_name (string): Official SCOB PDF filename (e.g. 1_SCOB_HCD_1.pdf).
• case_title (string): Title of the parties, jurisdiction case number, and formal citation.
• scob_issue (string): Official SCOB volume / edition (e.g. 1 SCOB [2015] HCD).
• publication_year (string): Publication year.
• text_source (string): Text origin (translated_english for translated vernacular rulings, original_ocr for English judgments).
• full_judgment (string): Full cleaned substantive judgment text starting from the court's operative opinion (Preamble, roster, and lawyer listings removed).
• judgment_body (string): Cleaned operative judgment text.
• short_ratio (string): Ground-truth ratio decidendi published in the official SCOB headnotes.

Configuration 2: sft (Chat / SFT Instruction Format)

Formatted for direct compatibility with Hugging Face Chat Templates (apply_chat_template) and TRL:

{
  "messages": [
    {
      "role": "user",
      "content": "Below is a judgment delivered by the High Court Division of the Supreme Court of Bangladesh.\n\nCase: Sheikh Ferozur Rahman Vs The State...\n\nJudgment Text:\nMd. Ruhul Quddus, J:...\n\nSummarize this judgment into its concise Short Ratio (the core legal principle / ratio decidendi settled by the Court)."
    },
    {
      "role": "assistant",
      "content": "An order of suspension does not terminate an employee's service, and the relationship of master and servant continues... (Para 15)"
    }
  ]
}

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Quickstart & Usage

1. Load with Hugging Face datasets

from datasets import load_dataset

# Load default tabular dataset
dataset = load_dataset("Hasin2026/bangladesh-scob-judgment-summarization", name="default", split="train")
print(dataset[0]["case_title"])
print(dataset[0]["short_ratio"])

# Load instruction-tuned SFT dataset for fine-tuning
sft_dataset = load_dataset("Hasin2026/bangladesh-scob-judgment-summarization", name="sft", split="train")
print(sft_dataset[0]["messages"])

2. Fine-Tuning with TRL SFTTrainer (e.g. Gemma 2 / Llama 3)

from datasets import load_dataset
from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments

model_id = "google/gemma-2-9b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

dataset = load_dataset("Hasin2026/bangladesh-scob-judgment-summarization", name="sft", split="train")

training_args = TrainingArguments(
    output_dir="./gemma-scob-summarizer",
    per_device_train_batch_size=1,
    gradient_accumulation_steps=8,
    learning_rate=2e-5,
    num_train_epochs=3,
    logging_steps=10,
    fp16=True,
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    args=training_args,
)

trainer.train()

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Curation & Preprocessing Pipeline

1. High-Fidelity OCR: Extracted page-by-page from original PDF documents using Gemini multimodal vision extraction, preserving statutory formatting, paragraph markers, and bench structures.
2. Multilingual Translation & Alignment: Judgments rendered in Bengali were translated into formal judicial English using chunk-aligned mapping without omission or truncation.
3. Preamble & Roster Stripping: Cause titles, lawyer appearance listings, and metadata headers were excised to ensure the model trains directly on substantive reasoning, evidence appraisal, and ratio decidendi.
4. Typography & Unicode Normalization:
   • Fixed inverted Bengali rephs and broken Bijoy glyph ligatures.
   • Replaced ambiguous Unicode punctuation (curly quotes, smart apostrophes, non-breaking spaces).
   • Removed OCR artifact control characters (\x0c, \x0b).

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Citation & Licensing

• License: Creative Commons Attribution 4.0 International (CC BY 4.0).
• Source: Supreme Court Online Bulletin (SCOB), Supreme Court of Bangladesh.

If you use this dataset in your research or application, please cite:

@dataset{bangladesh_scob_summarization_2026,
  title={Bangladesh Supreme Court (SCOB) High Court Division Judgment Summarization Dataset},
  author={Legal NLP Research & Preprocessing Pipeline},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets}}
}