nassimjp/pashto-wikipedia-sft

Pashto Wikipedia SFT Dataset

This dataset is a cleaned, structured, and context-anchored variant of the Pashto Wikipedia corpus, formatted explicitly for Supervised Fine-Tuning (SFT) and Continual Pre-Training (CPT).

By restructuring raw encyclopedic data into explicit source-grounding prompts, this dataset trains Large Language Models (LLMs) to couple their factual generation directly with reference variables (URLs and Titles), mitigating hallucination tendencies in low-resource language environments.

---

Dataset Profile & Statistics

A deep forensic scan of the processed corpus reveals a unique bi-modal length distribution, splitting heavily between succinct reference stub entries and massive historical/technical indexes.

Core Metrics

• Total Structured Records: 21,032 lines
• Raw File Size: ~116 MB
• Format: Agnostic JSONL (OpenAI ShareGPT/ChatML compatible messages)
• Estimated Token Volume: ~28M to 36M tokens (dependent on tokenizer sub-word splits like Qwen vs. Mistral)

Text Length Distribution Breakdown

Content Range (Characters)	Record Count	Percentage	Operational Profile
< 500 Chars	9,494	45.14%	Tier 1: Micro-Stub Articles (Placeholders/Definitions)
500 - 5,500 Chars	6,180	29.38%	Tier 2: Standard Informational Entries
5,501 - 20,000 Chars	5,077	24.14%	Tier 3: Comprehensive Long-Form Articles
> 20,001 Chars	281	1.34%	Tier 4: Exhaustive Tables and Composite Indexes

📌 Dataset Outlier Alert: The largest single document in this dataset is د پاکستاني تلويزوني چينلونو لړليک (List of Pakistani television channels), containing an intensive 222,719 characters.

---

Developer Target Guide: Slicing the 4 Tiers

To accommodate both quick-turnaround LoRA experimentation (under 4 hours) and advanced power users training deep context models, developers can use the Python partitioning snippet below to split the master pashto_wikipedia_sft.jsonl file into hardware-targeted training files:

import json

def partition_dataset():
    input_file = "pashto_wikipedia_sft.jsonl"
    
    tiers = {
        "tier1_micro_stubs": open("pashto_sft_tier1_micro_stubs.jsonl", "w", encoding="utf-8"),
        "tier2_standard_prose": open("pashto_sft_tier2_standard_prose.jsonl", "w", encoding="utf-8"),
        "tier3_long_context": open("pashto_sft_tier3_long_context.jsonl", "w", encoding="utf-8"),
        "tier4_ultra_power": open("pashto_sft_tier4_ultra_power.jsonl", "w", encoding="utf-8")
    }
    
    with open(input_file, "r", encoding="utf-8") as f:
        for line in f:
            data = json.loads(line)
            content_len = len(data["messages"][1]["content"])
            
            if content_len < 500:
                tiers["tier1_micro_stubs"].write(line)
            elif 500 <= content_len <= 5500:
                tiers["tier2_standard_prose"].write(line)
            elif 5501 < content_len <= 20000:
                tiers["tier3_long_context"].write(line)
            else:
                tiers["tier4_ultra_power"].write(line)
                
    for fh in tiers.values():
        fh.close()

if __name__ == "__main__":
    partition_dataset()

⏱️ Tier 1: Micro-Stubs (pashto_sft_tier1_micro_stubs.jsonl)

• Size: 9,494 records (< 500 characters per sample)
• Target Audience: Quick hardware smoke-tests, vocabulary embedding alignment, or CPU/low-end GPU debugging. Highly dependent on packing to prevent padding overhead.

🚀 Tier 2: Standard Prose (pashto_sft_tier2_standard_prose.jsonl)

• Size: 6,180 records (500–5,500 characters per sample)
• Target Audience: The 4-hour LoRA experimentation crowd. It skips micro-stubs and massive list pages, containing high-quality, mid-length encyclopedic descriptions that map out grammar structures cleanly. Fits natively into standard 2048 context windows for ultra-fast iterations on consumer hardware.

🏋️ Tier 3: Long-Context (pashto_sft_tier3_long_context.jsonl)

• Size: 5,077 records (5,501–20,000 characters per sample)
• Target Audience: Base model alignment and full parameter tuning. These heavy historical documents force the model to maintain attention coherence across 2048 to 4096 token windows.

⚡ Tier 4: Ultra Power (pashto_sft_tier4_ultra_power.jsonl)

• Size: 281 records (> 20,000 characters per sample)
• Target Audience: Power users testing high-end context extension capabilities (FlashAttention-2, 8192+ context windows, high-VRAM clusters). Contains massive raw tables, deep technical lists, and long composite directories.

---

Dataset Structure

The dataset utilizes the universally supported chat/messages schema. It contains zero hardcoded tokenizer tags (no <s>, [INST], or <|im_start|>), leaving formatting templates to be handled entirely at runtime by your training framework.

Schema Example

{
  "messages": [
    {
      "role": "user",
      "content": "ماخذ او سرچینه:\nعنوان: افغانستان\nپته: [https://ps.wikipedia.org/wiki/افغانستان](https://ps.wikipedia.org/wiki/افغانستان)\nلاندې متن په غور سره ولولئ او په خپلو هغو کې خوندي کړئ:"
    },
    {
      "role": "assistant",
      "content": "افغانستان په سوېل کې پروت دی..."
    }
  ]
}

---

Key Modifications & Data Hygiene

1. Source Grounding Architecture: Every block of text is cleanly prefaces with structured metadata fields (ماخذ او سرچینه, عنوان, پته) inside the user command to tightly bind generation to strict source conditions.
2. Boilerplate and Template Stripping: Automated text cleaning passes matched and stripped trailing Wikipedia metadata blocks and structural footers (e.g., سرچينې, باندنۍ تړنې, دا هم وګورئ, دا هم وگورۍ). This effectively isolates pure Pashto text prose and shields the model from learning loop-inducing artifacts.
3. Encoding Verification: Fully structured using valid UTF-8 configurations with unescaped native Pashto script rendering directly inside the .jsonl payloads for direct, unhindered tokenization passes.

---

Technical Training Recommendations

1. Sequence Packing (Critical)

Because 45.14% of the total master dataset sits under 500 characters, traditional training without sequence packing will result in enormous padding inefficiency.

• Action: Enable sample packing (packing: true in TRL/Axolotl or equivalent multiplex token configurations). This will append these disparate sequences continuously up to your desired target width (e.g., sequence_len: 4096), compressing VRAM utilization and multiplying step execution speed by nearly 2x.

2. Loss Masking Optimization

• Action: Set your configuration to skip calculating gradients on input sequences (train_on_inputs: false). This guarantees that the model focuses its mathematical updates strictly on generating the clean Pashto language contents of the assistant blocks, rather than memorizing system prompts or structural instructions.

---

Additional Information & Attributions

• Source Attribution: This dataset is a curated, cleaned, and structurally modified derivative of the upstream repository ihanif/pashto-wikipedia-corpus, compiled, processed, and hosted originally by Hanif Rahman on July 21, 2024.
• Temporal Note: This corpus reflects a historical, static snapshot captured around that 2024 frame and does not dynamically correlate with active or modern edits live on the Pashto Wikipedia platform.

License

This dataset is distributed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license, aligning entirely with the upstream legal and source data governance requirements set forth by the Wikimedia Foundation.