| --- |
| license: apache-2.0 |
| task_categories: |
| - image-to-text |
| - visual-question-answering |
| tags: |
| - vlm |
| - unsloth |
| - qwen-vl |
| - document-ai |
| - trade-finance |
| - bangladesh-bank |
| size_categories: |
| - n<1K |
| --- |
| |
| # 🏦 Bangladesh Bank Trade Finance IDP — Multi-User Collaborative Fine-Tuning Dataset |
|
|
| This dataset contains human-reviewed, verified, and corrected document extractions for the **8 official Bangladesh Bank regulatory trade-finance document types**. |
|
|
| It is **completely self-contained** and structured for immediate Vision-Language Model (VLM) fine-tuning anytime from any environment (Colab, Kaggle, GPU cluster, or local), with built-in multi-annotator merge support and incremental delta uploads. |
|
|
| --- |
|
|
| ## 📊 Dataset Structure & Splits |
|
|
| | Split / Subset | Description | Examples | |
| |---|---|---| |
| | **`train`** | Ready-to-train multi-task VLM conversations (`messages` format) | 320 | |
| | **`validation`** | Stratified held-out evaluation conversations for monitoring eval loss | 80 | |
| | **`documents`** | Full document-level feedback records with model outputs, human corrections, confidence scores & DPO pairs (subset: `documents`) | 200 | |
|
|
| ### 📑 Document Types Represented |
| | Document Type | Document Count | |
| |---|---| |
| | `air_waybill` | 25 | |
| | `bill_of_entry` | 25 | |
| | `commercial_lca` | 25 | |
| | `exp_form` | 25 | |
| | `final_invoice` | 25 | |
| | `imp_form` | 25 | |
| | `industrial_lca` | 25 | |
| | `ocean_bl` | 25 | |
|
|
| **Total DPO Preference Pairs Available:** 10 (for Direct Preference Optimization) |
|
|
| - **Visual Diversity:** 8 Header styles, 5 Table formats, 4 Procedural Stamp configurations, and 12 distinct color palettes. |
| - **Realistic Handwriting:** Natural handwriting pen fills and endorsements in varied inks (blue ballpoint, dark ink, navy, royal blue) paired with 100% strictly validated schema JSON. |
| - **Header Integrity:** `MessageIdentifier` is strictly excluded from document image layouts and is normalized as empty `""` in ground truth. |
|
|
| --- |
|
|
| ## 🚀 Quickstart: Train in 10 Lines with Unsloth / TRL |
|
|
| You can fine-tune Qwen3-VL / Qwen2.5-VL directly on this dataset without ANY manual data wrangling: |
|
|
| ```python |
| from datasets import load_dataset |
| from unsloth import FastVisionModel |
| from trl import SFTTrainer, SFTConfig |
| |
| # 1. Load dataset directly from Hugging Face |
| ds = load_dataset("bisalsaha/bb-trade-idp-feedback") |
| train_data = ds["train"] |
| eval_data = ds.get("validation") |
| |
| # 2. Load model & attach vision LoRA adapters |
| model, tokenizer = FastVisionModel.from_pretrained( |
| "unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit", |
| load_in_4bit=True |
| ) |
| model = FastVisionModel.get_peft_model( |
| model, |
| r=16, |
| lora_alpha=32, |
| target_modules=["q_proj", "v_proj"] |
| ) |
| |
| # 3. SFT Trainer |
| trainer = SFTTrainer( |
| model=model, |
| train_dataset=train_data, |
| eval_dataset=eval_data, |
| dataset_text_field="messages", |
| max_seq_length=2048, |
| args=SFTConfig( |
| per_device_train_batch_size=2, |
| gradient_accumulation_steps=4, |
| learning_rate=1e-4, |
| output_dir="./qwen3vl_trade_idp" |
| ) |
| ) |
| trainer.train() |
| ``` |
|
|
