| --- |
| license: apache-2.0 |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| base_model: BAAI/bge-small-en-v1.5 |
| datasets: |
| - gyaanbyte/coa-mapping-synthetic |
| language: |
| - en |
| tags: |
| - finance |
| - accounting |
| - chart-of-accounts |
| - data-migration |
| - quickbooks |
| - sage |
| - retrieval |
| - sentence-transformers |
| model-index: |
| - name: coa-mapper |
| results: |
| - task: |
| type: sentence-similarity |
| name: Chart-of-accounts mapping (retrieval) |
| dataset: |
| name: COA-Map-Bench v1 (gyaanbyte/coa-mapping-synthetic, test) |
| type: gyaanbyte/coa-mapping-synthetic |
| split: test |
| metrics: |
| - type: accuracy |
| name: acc@1 |
| value: 0.8832 |
| - type: accuracy |
| name: acc@5 |
| value: 0.9635 |
| - type: mrr |
| name: MRR |
| value: 0.9239 |
| --- |
| |
| # COA Mapper v1 |
|
|
| The first open model for **chart-of-accounts mapping**: given a ledger account |
| from QuickBooks Desktop or Sage 50 (name + type), retrieve the correct target |
| account in the client's new QuickBooks Online chart of accounts. |
|
|
| A 33M-parameter embedding model (`BAAI/bge-small-en-v1.5`, fine-tuned) — |
| small enough to run on CPU, in a browser, or inside a migration tool. |
|
|
| ## Results — COA-Map-Bench v1 |
|
|
| Test split of [`gyaanbyte/coa-mapping-synthetic`](https://huggingface.co/datasets/gyaanbyte/coa-mapping-synthetic): |
| 137 mappings from 4 held-out companies (2 QuickBooks Desktop, 2 Sage 50) never |
| seen in training. Candidates = the distinct target accounts of the same company |
| (i.e. mapping into the client's new QBO COA). A top-1 hit implies the correct |
| QBO type *and* detail type. |
|
|
| | Method | acc@1 | acc@5 | MRR | |
| |---|---|---|---| |
| | TF-IDF (char 2–4-grams, cosine) | 0.745 | 0.949 | 0.824 | |
| | `bge-small-en-v1.5` zero-shot | 0.796 | 0.956 | 0.868 | |
| | qwen3:8b zero-shot (picks from the candidate list) | 0.788 | — | — | |
| | **coa-mapper v1 (this model)** | **0.883** | **0.964** | **0.924** | |
|
|
| Read the numbers honestly: the benchmark is synthetic, and account names carry |
| strong lexical signal (TF-IDF already gets 74.5%). The fine-tune's gain lives in |
| the hard 25% — abbreviations, near-duplicates to consolidate, dumping-ground |
| splits, type/detail-type disambiguation. An adjudicated benchmark subset is |
| planned for v1.1. |
|
|
| ## Try it |
|
|
| 🧪 **[Live demo](https://huggingface.co/spaces/gyaanbyte/coa-mapper-demo)** — runs in your browser via Transformers.js (int8 ONNX included in this repo under `onnx/`; nothing is uploaded). |
|
|
| ## Usage |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| m = SentenceTransformer("gyaanbyte/coa-mapper") |
| |
| # Source accounts as "name (type) [system]"; targets as "name — type / detail type" |
| src = ["Accum Depr - Equipment (fixed asset) [quickbooks desktop]"] |
| targets = [ |
| "Accumulated Depreciation — Fixed Assets / Accumulated Depreciation", |
| "Equipment — Fixed Assets / Machinery & Equipment", |
| "Depreciation Expense — Other Expense / Depreciation", |
| ] |
| S = m.encode(src, normalize_embeddings=True) |
| T = m.encode(targets, normalize_embeddings=True) |
| print(targets[(S @ T.T).argmax()]) |
| ``` |
|
|
| In production we use it as **retrieval → LLM re-rank of the top-5 → confidence |
| threshold → human review lane**: the goal is hours saved per migration at $0 |
| reconciliation variance, not unattended automation. |
|
|
| ## Training |
|
|
| - Base: `BAAI/bge-small-en-v1.5` · loss: MultipleNegativesRankingLoss (in-batch negatives) |
| - Data: 1,025 (source → target) pairs, train split of the dataset above |
| - 4 epochs, batch 32, lr 3e-5, fp16 — **12 seconds on one RTX 5060 laptop GPU** |
| - Training/eval code: [`coa_train.py`](https://github.com/Divyanshujangra/gyaanbyte-infra) (open) |
|
|
| ## Limitations |
|
|
| - Trained and evaluated on synthetic data; real ledgers are messier and more idiosyncratic |
| - Target side is QuickBooks Online only; sources are QuickBooks Desktop and Sage 50 |
| - English, US-GAAP-oriented account structures |
| - Candidate pool must be supplied (the target COA); this model does not invent accounts |
|
|
| ## Who's building this |
|
|
| [GyaanByte Labs](https://www.gyaanbytelabs.com) — accounting data-migration |
| specialists (6+ production migrations, 200,000+ records, $0 variance). The |
| mapper is the engine behind our migration service; the reusable parts are |
| published openly. |
|
|
