| --- |
| license: other |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| datasets: |
| - finipher/finipher-us-tax-document-classifier |
| pipeline_tag: text-classification |
| language: |
| - en |
| tags: |
| - finance |
| - tax |
| - document-classification |
| --- |
| |
| # Finipher tax document classifier v1.0.0 |
|
|
| Names what a US tax or financial document is (W-2, 1098, 1099-INT, mortgage statement, paystub, |
| brokerage statement and so on) from the first 128 tokens of its extracted text. 95 labels. |
|
|
| It is a small head on a frozen MiniLM encoder. The head only works with the exact encoder it was |
| trained on, so this repo carries its own copy of that encoder (`model.safetensors`, SHA-256 |
| `96d95c25…`). That file also holds a transaction output layer, which this model ignores. |
|
|
| Internal names: checkpoint `finance-multitask-minilm-two-head-v2`; head artifact |
| `tax-document-head-20261027-v1.1-category-encoder` (stored inside `tax_document_head_config.json`). |
|
|
| ## How to use |
|
|
| The head is custom, so load it with this code (the same steps Finipher runs in production): |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| from safetensors.torch import load_file |
| from transformers import AutoModel, AutoTokenizer |
| import json, torch |
| |
| path = snapshot_download("finipher/finipher-tax-document-classifier", revision="v1.0.0") |
| tok = AutoTokenizer.from_pretrained(path) |
| enc = AutoModel.from_pretrained(path).eval() # ignores the transaction output layer |
| head = load_file(f"{path}/tax_document_head.safetensors") |
| cfg = json.load(open(f"{path}/tax_document_head_config.json")) |
| labels = [cfg["id2label"][str(i)] for i in range(cfg["num_labels"])] |
| |
| def classify(text: str): |
| x = tok([text.strip()[:4000]], truncation=True, max_length=128, return_tensors="pt") |
| with torch.inference_mode(): |
| h = enc(**x).last_hidden_state |
| m = x["attention_mask"].unsqueeze(-1).float() |
| e = torch.nn.functional.normalize((h * m).sum(1) / m.sum(1), dim=-1) |
| p = torch.softmax(e @ head["weight"].T + head["bias"], dim=-1)[0] |
| i = int(p.argmax()) |
| return labels[i], float(p[i]), float(p[i]) >= cfg["review_threshold"] # 0.76 |
| ``` |
|
|
| The third value says whether the label is confident enough to use (0.76 or higher). Below that, |
| send the document to a person to review. `OTHER_TAX_FORM` and `UNKNOWN_DOCUMENT` always need review. |
|
|
| ## Results |
|
|
| Measured offline through Finipher's production code: |
| - Test set (2,531 documents): 98.5%. |
| - Same set with the titles cut off (1,203): 94.7%. |
| - Blind probes (33): 90.9%. |
|
|
| ## Known mistakes |
|
|
| - Credit card statement CSV exports read as BANK_STATEMENT (confidence 0.51–0.71, so usually sent to review). |
| |
| ## Training data |
| |
| `finipher/finipher-us-tax-document-classifier` at revision `92c67c9` (v1.1.0), licensed CC-BY-4.0. |
| The encoder underneath also comes from the transaction model, whose training data is still to be |
| confirmed. This repo stays private until that is documented. |
| |
| ## Use |
| |
| The output names a document type. It is not tax or financial advice. |
| |
| ## Files and SHA-256 |
| |
| | File | SHA-256 | |
| |---|---| |
| | `config.json` | `6b8c456706017e7cbddd8a4826a88746294100336be5aa9e40f7730890f1b72b` | |
| | `model.safetensors` | `96d95c2520f9b8cee0a3e85e1440f541420c4c043e18a0c4579943e08e91a5a3` | |
| | `tokenizer.json` | `41b9d12a4c559e4098f30173bc0f76d092c43d7b1f873e7027da45a256f30f87` | |
| | `tokenizer_config.json` | `1128dd50ff3f913671a9405619c1bd8f9e9ddc28f62257cb94ed70683590b715` | |
| | `tax_document_head.safetensors` | `bb60aaad3734afda9120757dec4e2ed60ae04c6ba93f5993a3cb0c893ee4ac35` | |
| | `tax_document_head_config.json` | `41600002856c26fc30bb47d2c6e40b3c8a0352b77220c1e21c3d6c3e57594c8b` | |
|
|
