| --- |
| license: apache-2.0 |
| base_model: distilroberta-base |
| language: en |
| pipeline_tag: text-classification |
| library_name: transformers |
| tags: [finance, accounting, spend-classification, procurement, onnx] |
| metrics: [accuracy, f1] |
| --- |
| |
| # SpendCoder: purchase line to spend category |
|
|
| Fine-tuned **distilroberta-base** that reads a short purchase or invoice line (for example |
| "ANNUAL MAINTENANCE OF HV SWITCHGEAR") and returns one of 14 spend categories, with a confidence score. |
| Low-confidence lines are meant to go to a person. Code, data preparation and tests: https://github.com/saeedm31/SpendCoder |
|
|
| ## Results (test year, contracts the model never saw) |
|
|
| | Model | Accuracy | Macro F1 | Weighted F1 | |
| |---|---|---|---| |
| | TF-IDF + logistic regression (baseline) | 0.673 | 0.616 | 0.675 | |
| | Fine-tuned distilroberta-base | 0.665 | 0.569 | 0.664 | |
|
|
| At the chosen review threshold (0.99), 29.0% of test lines are coded automatically with 91.3% accuracy; the rest go to a person. |
|
|
| Train: 17,098 lines (contracts starting before Oct 2023). Validation: 2,065 |
| (Oct 2023 to Sep 2024). Test: 1,547 (from Oct 2024). Test lines whose exact text appears in |
| training were removed. |
|
|
| ## Categories |
| - Engineering & professional services |
| - Research & development |
| - Facility operations & management |
| - Construction & building maintenance |
| - Equipment maintenance & repair |
| - IT & telecom services |
| - Environmental services |
| - Utilities, security & housekeeping |
| - Other services |
| - IT hardware & software |
| - Electrical & power equipment |
| - Lab instruments & chemicals |
| - Vehicles & machinery |
| - Other goods |
|
|
| ## Data |
| US Department of Energy contract descriptions and their Product and Service Codes from |
| [USAspending.gov](https://www.usaspending.gov) (public domain), grouped into the 14 categories above. |
|
|
| ## Use |
| Suggest a spend category for purchase and invoice lines in accounts payable or procurement; send lines below the |
| review threshold to a person. Not for automatic posting without review. Trained on one public-sector buyer's |
| wording: before using it in another company, check it on that company's labelled lines and fine-tune further. |
|
|
| ## Files |
| - PyTorch / Transformers weights at the repo root: `AutoModelForSequenceClassification.from_pretrained(...)` |
| - `onnx/`: `model.onnx`, `tokenizer.json`, `spendcoder.json` (labels, review threshold) for CPU serving with |
| onnxruntime, no PyTorch needed |
|
|
