Datasets:
Formats:
json
Languages:
English
Size:
10K - 100K
Tags:
three-way-matching
accounts-payable
procurement
invoice-processing
purchase-order
goods-receipt-note
License:
| license: mit | |
| task_categories: | |
| - document-question-answering | |
| - image-to-text | |
| - text-classification | |
| - table-question-answering | |
| language: | |
| - en | |
| tags: | |
| - three-way-matching | |
| - accounts-payable | |
| - procurement | |
| - invoice-processing | |
| - purchase-order | |
| - goods-receipt-note | |
| - ocr | |
| - multimodal | |
| - document-ai | |
| - agent-benchmark | |
| - synthetic | |
| pretty_name: Three-Way Matching Multimodal Agent Benchmark (2,500 Cases / 7,500 Documents) | |
| size_categories: | |
| - 1K<n<10K | |
| configs: | |
| - config_name: cases | |
| default: true | |
| data_files: | |
| - split: train | |
| path: data/cases_train.jsonl | |
| - split: test | |
| path: data/cases_test.jsonl | |
| - config_name: documents | |
| data_files: | |
| - split: train | |
| path: data/documents_train.jsonl | |
| - split: test | |
| path: data/documents_test.jsonl | |
| # Three-Way Matching Multimodal Agent Benchmark (2,500 Cases / 7,500 Documents) | |
| A multimodal Accounts Payable (AP) & Procurement **Three-Way Matching** benchmark dataset designed for evaluating LLM/VLM agents, OCR pipelines, and document-understanding workflows across **Purchase Orders (PO)**, **Warehouse Receipts / Goods Receipt Notes (GRN)**, and **Vendor Invoices**. | |
| ## Dataset Highlights | |
| - **2,500 Complete Procurement Cases** (`train`: 2,000 cases, `test`: 500 cases — stratified across all 13 matching & exception scenarios) | |
| - **7,500 Multimodal Documents** (`train`: 6,000 docs, `test`: 1,500 docs — `1,230.7 MB` total) | |
| - **Mixed Formats Inside Every Triplet**: Every standard 3-document packet combines **Word (`.docx`)**, **PDF (`.pdf`)**, and **Scanned Image (`.jpg` / `.png`)**, rotating which document role receives which format across cases. | |
| - **Both Vector & Scanned PDFs**: Includes selectable digital ERP PDFs (`vector_text_pdf`) as well as rasterized scanned PDFs (`scanned_image_pdf`). | |
| - **Realistic Visual & Dock Artifacts**: Image and scanned-PDF documents feature page skew (`±0.5°` to `±2.1°`), scanner drop shadows, rubber ink stamps (`RECEIVED & VERIFIED`, `SHORT SHIPMENT NOTED`, `DAMAGED GOODS REJECTED`, `AP RECEIVED`), vector barcodes, paper speckles/creases, and ballpoint handwritten dock clerk notes + cursive signatures. | |
| - **Dual Ground-Truth Verdicts**: Each case provides both `strict_verdict` (zero-variance matching) and `tolerance_verdict` (standard AP tolerance policy: `≤ $15.00` or `≤ 1%` Prepaid & Add freight, valid partial-delivery billing allowed, missing receipts held, duplicate invoices blocked). | |
| --- | |
| ## Quickstart with Hugging Face `datasets` | |
| ```python | |
| import json | |
| from datasets import load_dataset | |
| from huggingface_hub import snapshot_download | |
| # 1. Load structured case-level benchmark splits (default config: 'cases') | |
| cases = load_dataset("DevLCodes/three-way-matching-dataset", "cases") | |
| print(cases) | |
| sample = cases["test"][0] | |
| print(sample["case_id"], sample["scenario"], "->", sample["tolerance_verdict"]) | |
| print("Files:", sample["file_paths"]) | |
| print("Discrepancies:", json.loads(sample["discrepancies_json"])) | |
| # 2. Load document-level index ('documents' config — all 7,500 files) | |
| docs = load_dataset("DevLCodes/three-way-matching-dataset", "documents") | |
| # 3. Download raw DOCX, PDF, PNG, and JPG files locally for your agent | |
| repo_dir = snapshot_download("DevLCodes/three-way-matching-dataset", repo_type="dataset") | |
| ``` | |
| --- | |
| ## Scenario Taxonomy (2,500 Cases) | |
| | Scenario | Cases | Share | | |
| | :--- | ---: | ---: | | |
| | `damaged_rejected_goods` | 150 | 6.0% | | |
| | `duplicate_invoice` | 125 | 5.0% | | |
| | `exact_match` | 550 | 22.0% | | |
| | `invoice_math_or_tax_error` | 150 | 6.0% | | |
| | `missing_warehouse_receipt` | 125 | 5.0% | | |
| | `over_shipment_billed` | 150 | 6.0% | | |
| | `short_shipment_overbilled` | 250 | 10.0% | | |
| | `short_shipment_properly_billed` | 150 | 6.0% | | |
| | `unapproved_freight_or_surcharge` | 175 | 7.0% | | |
| | `unit_price_hike` | 250 | 10.0% | | |
| | `unordered_line_item` | 125 | 5.0% | | |
| | `within_tolerance_match` | 200 | 8.0% | | |
| | `wrong_po_or_vendor_mismatch` | 100 | 4.0% | | |
| ### Scenario Descriptions | |
| 1. **`exact_match`**: PO, Warehouse Receipt, and Vendor Invoice align 100% on SKUs, quantities, unit prices, tax, and totals (includes realistic vendor description phrasing & date-format variations). | |
| 2. **`within_tolerance_match`**: Small `FOB Origin, Prepaid & Add` shipping charge (`≤ $12.00`) within the `$15.00 / 1%` AP tolerance threshold (`strict_verdict = HOLD_VARIANCE`, `tolerance_verdict = APPROVED_WITHIN_TOLERANCE`). | |
| 3. **`short_shipment_overbilled`**: Warehouse received fewer units than ordered (with dock clerk note), yet Vendor Invoice billed the full PO quantity. | |
| 4. **`short_shipment_properly_billed`**: Partial shipment received at dock and Vendor Invoice accurately bills *only* the delivered quantity (`APPROVED_PARTIAL_DELIVERY`). | |
| 5. **`over_shipment_billed`**: Vendor shipped and billed >15–35% extra units beyond PO authorization without a PO amendment. | |
| 6. **`damaged_rejected_goods`**: Full quantity arrived at dock, but damaged units were rejected during inspection (`qty_accepted < qty_received`) while Invoice bills full quantity. | |
| 7. **`unit_price_hike`**: Invoice unit price exceeds contracted PO unit price by +8% to +22%. | |
| 8. **`unapproved_freight_or_surcharge`**: PO terms specify `FOB Destination (Freight Included)`, but Invoice adds unauthorized freight/surcharge (`$185–$550`). | |
| 9. **`invoice_math_or_tax_error`**: Explicit line-extension (`qty * unit_price != line_total`) or header summation arithmetic error on the Invoice. | |
| 10. **`unordered_line_item`**: Invoice bills an extra SKU/line item never ordered on the PO or logged on the Warehouse Receipt. | |
| 11. **`missing_warehouse_receipt`**: Only PO and Invoice are present (2 files); goods receipt is missing/unconfirmed (`HOLD_MISSING_RECEIPT`). | |
| 12. **`wrong_po_or_vendor_mismatch`**: Invoice references a non-matching PO number or is issued by a different legal vendor entity / Tax ID than the PO. | |
| 13. **`duplicate_invoice`**: Packet contains **4 files** — PO, Warehouse Receipt, primary Invoice, and a second duplicate/re-billed Invoice against the same shipment (`REJECT_DUPLICATE_INVOICE`). | |
| --- | |
| ## File Format & Render Mode Distribution (7,500 Documents) | |
| | Format | Count | Share | | |
| | :--- | ---: | ---: | | |
| | `DOCX` | 2,451 | 32.7% | | |
| | `JPG` | 1,346 | 17.9% | | |
| | `PDF` | 2,453 | 32.7% | | |
| | `PNG` | 1,250 | 16.7% | | |
| | Render Mode | Count | Share | | |
| | :--- | ---: | ---: | | |
| | `openxml_docx` | 2,451 | 32.7% | | |
| | `scanned_image_pdf` | 558 | 7.4% | | |
| | `scanned_jpg` | 1,346 | 17.9% | | |
| | `scanned_png` | 1,250 | 16.7% | | |
| | `vector_text_pdf` | 1,895 | 25.3% | | |
| --- | |
| ## Repository Structure | |
| ```text | |
| . | |
| ├── README.md # Hugging Face Dataset Card & metadata | |
| ├── .gitattributes # Git LFS configuration for binary docs/images | |
| ├── ground_truth.json # Complete nested dataset & AP tolerance policy JSON | |
| ├── manifest.csv # Case-level summary spreadsheet (2,500 rows) | |
| ├── documents_index.csv # Document-level index (7,500 rows + SHA-256) | |
| ├── data/ | |
| │ ├── cases_train.jsonl # 2,000 training cases | |
| │ ├── cases_test.jsonl # 500 evaluation cases | |
| │ ├── documents_train.jsonl # 6,000 training document records | |
| │ └── documents_test.jsonl # 1,500 evaluation document records | |
| └── documents/ | |
| ├── CASE_0001/ # Mixed DOCX + PDF + PNG/JPG packet per case | |
| ... | |
| └── CASE_2500/ | |
| ``` | |
| ## License | |
| Released under the **MIT License**. All companies, addresses, tax IDs, SKUs, and transactions are 100% synthetic. | |