| --- |
| 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) | 334 | |
| | **`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`) | 207 | |
|
|
| ### 📑 Document Types Represented |
| | Document Type | Document Count | |
| |---|---| |
| | `air_waybill` | 27 | |
| | `bill_of_entry` | 26 | |
| | `commercial_lca` | 25 | |
| | `exp_form` | 25 | |
| | `final_invoice` | 28 | |
| | `imp_form` | 25 | |
| | `industrial_lca` | 25 | |
| | `ocean_bl` | 26 | |
|
|
| **Total DPO Preference Pairs Available:** 1 (for Direct Preference Optimization) |
|
|
| --- |
|
|
| ## 🚀 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 unsloth.trainer import UnslothVisionDataCollator |
| from trl import SFTTrainer, SFTConfig |
| |
| # 1. Load the ready-to-train dataset directly from Hugging Face |
| ds = load_dataset("jihadv4/bb-trade-idp-feedback_V1") |
| train_data = ds["train"] |
| eval_data = ds.get("validation") |
| |
| # 2. Load model & attach vision LoRA adapters |
| model, tokenizer = FastVisionModel.from_pretrained( |
| "unsloth/Qwen3-VL-Max-Instruct", |
| load_in_4bit=True, |
| max_seq_length=2048, |
| ) |
| model = FastVisionModel.get_peft_model( |
| model, |
| finetune_vision_layers=True, |
| finetune_language_layers=True, |
| finetune_attention_modules=True, |
| finetune_mlp_modules=True, |
| r=16, |
| lora_alpha=16, |
| lora_dropout=0.05, |
| ) |
| FastVisionModel.for_training(model) |
| |
| # 3. Train with SFTTrainer |
| trainer = SFTTrainer( |
| model=model, |
| tokenizer=tokenizer, |
| data_collator=UnslothVisionDataCollator(model, tokenizer), |
| train_dataset=train_data, |
| eval_dataset=eval_data, |
| args=SFTConfig( |
| per_device_train_batch_size=2, |
| gradient_accumulation_steps=4, |
| learning_rate=1e-4, |
| num_train_epochs=3, |
| optim="adamw_8bit", |
| output_dir="./lora_checkpoints", |
| ), |
| ) |
| trainer.train() |
| ``` |
|
|
| --- |
|
|
| ## 📦 Standalone Artifacts in this Repository |
|
|
| - **`images/`**: Content-addressed directory containing every verified document page image (LFS deduplicated). |
| - **`images_backup.tar.gz`**: Standalone compressed archive of all page images for raw PyTorch / non-parquet workflows. |
| - **`feedback.jsonl`**: Exact raw JSONL feedback log with per-record `batch_id` and `synced_at` watermarks. |
| - **`schemas_bundle.json`**: Official Bangladesh Bank JSON schemas, field definitions, and XML templates. |
| - **`trade_ontology.json`**: Concept synonym mappings and bilingual hints. |
| - **`training_config.json`**: Recommended hyperparameters, target modules, and runtime compatibility settings. |
|
|
