| --- |
| license: apache-2.0 |
| base_model: mockgen-word-sgd-v1 |
| library_name: onnxruntime |
| pipeline_tag: text-classification |
| tags: |
| - mock-data |
| - semantic-classification |
| - enterprise-schemas |
| widget: |
| - text: |- |
| Entity: JournalEntryItem |
| Field: SAKNR |
| Label: G/L Account |
| Type: string |
| Related: CompanyCode, FiscalYear, AmountInCompanyCodeCurrency |
| example_title: "SAP G/L account field" |
| output: |
| - label: gl_account |
| score: 0.6323356628417969 |
| - label: country |
| score: 0.02091727964580059 |
| - label: fiscal_period |
| score: 0.012878832407295704 |
| - text: |- |
| Entity: AccountingDocument |
| Field: BUKRS |
| Label: Company Code |
| Type: string |
| Related: CompanyCodeName, Country, Currency, ChartOfAccounts |
| example_title: "Company code field" |
| output: |
| - label: company_code |
| score: 0.6074062585830688 |
| - label: currency |
| score: 0.1046433299779892 |
| - label: country |
| score: 0.07778121531009674 |
| - text: |- |
| Entity: SupplierInvoice |
| Field: ZTERM |
| Label: Terms of Payment |
| Type: string |
| Related: Supplier, InvoiceAmount, DueDate, PaymentBlockingReason |
| example_title: "Payment terms field" |
| output: |
| - label: payment_terms |
| score: 0.7714565992355347 |
| - label: country |
| score: 0.023687832057476044 |
| - label: currency |
| score: 0.010493538342416286 |
| - text: |- |
| Entity: MaterialDocumentItem |
| Field: WERKS |
| Label: Plant |
| Type: string |
| Related: StorageLocation, Material, QuantityInEntryUnit |
| example_title: "Plant field" |
| output: |
| - label: plant |
| score: 0.5506921410560608 |
| - label: material_no |
| score: 0.036951154470443726 |
| - label: country |
| score: 0.036014873534440994 |
| - text: |- |
| Entity: PurchaseOrderItem |
| Field: MSEHI |
| Label: Order Unit |
| Type: string |
| Related: Material, OrderQuantity, NetPriceAmount |
| example_title: "Order unit field" |
| output: |
| - label: unit_of_measure |
| score: 0.7284214496612549 |
| - label: quantity |
| score: 0.07117477804422379 |
| - label: purchase_order_no |
| score: 0.03281358629465103 |
| - text: |- |
| Entity: SalesOrder |
| Field: KUNNR |
| Label: Sold-To Party |
| Type: string |
| Related: SalesDocument, SalesOrganization, CustomerName, NetAmount |
| example_title: "Sold-to party field" |
| output: |
| - label: customer_id |
| score: 0.4585545063018799 |
| - label: org_name |
| score: 0.05491480976343155 |
| - label: country |
| score: 0.030577752739191055 |
| - text: |- |
| Entity: SalesOrder |
| Field: CurrencyCode |
| Label: Currency |
| Type: string |
| Related: NetAmount, GrossAmount, Country |
| example_title: "Currency field" |
| output: |
| - label: currency |
| score: 0.46692952513694763 |
| - label: country |
| score: 0.3333224654197693 |
| - label: org_name |
| score: 0.019802909344434738 |
| --- |
| |
| # MockGen Classifier v1 |
|
|
| ## Artifact status |
|
|
| The classifier is trained locally, export verified, and published to Hugging Face. This is the |
| accepted v1 classifier artifact. Experimental retraining runs are evaluated offline and only |
| replace this artifact when they improve both service-holdout and domain-holdout benchmarks. |
|
|
| ## Publication status |
|
|
| Public repository: https://huggingface.co/Unseen1980/mockgen-classifier-v1/tree/v1 |
|
|
| Published artifacts are the quantized ONNX classifier, this card, and non-secret metrics. The |
| publication-safe classifier dataset is published separately at |
| https://huggingface.co/datasets/Unseen1980/mockgen-classifier-data-v1/tree/v1. |
|
|
| The card includes `pipeline_tag: text-classification` and widget examples so the Hub can display |
| the classifier's task and sample outputs. Live browser inference depends on Hugging Face provider |
| support for this repository. For a dedicated Hugging Face Inference Endpoint, publish the included |
| `handler.py` and `requirements.txt`; the handler wraps the ONNX model with the same field-text |
| normalization used by MockGen. |
|
|
| ## Intended use |
|
|
| The classifier predicts MockGen semantic vocabulary hints for enterprise schema properties. The runtime uses those hints to route properties to retrieval banks, deterministic patterns, grounded templates, and validated fallbacks. |
|
|
| ## Local inference |
|
|
| The classifier can be tested without a Fiori app: |
|
|
| ```bash |
| npx tsx -e ' |
| import { readFileSync } from "node:fs"; |
| import { createOnnxClassifierRunner } from "./packages/mockgen-core/src/classifier-runner"; |
| |
| (async () => { |
| const report = JSON.parse(readFileSync("models/classifier/onnx/export-report.json", "utf8")); |
| const runner = await createOnnxClassifierRunner({ |
| modelPath: "packages/mockgen-models/artifacts/classifier.onnx", |
| hints: report.labels, |
| inputNormalizer: report.inputNormalizer |
| }); |
| |
| const result = await runner.classify({ |
| entity: "JournalEntryItem", |
| name: "SAKNR", |
| type: "string", |
| constraints: { nullable: true }, |
| semantic: { |
| hint: "unknown", |
| label: "G/L Account", |
| related: ["CompanyCode", "FiscalYear", "AmountInCompanyCodeCurrency"] |
| }, |
| values: [], |
| examples: [] |
| }); |
| |
| console.log(JSON.stringify({ |
| hint: result.hint, |
| confidence: result.confidence, |
| top: result.top.slice(0, 5) |
| }, null, 2)); |
| })(); |
| ' |
| ``` |
|
|
| Expected top prediction: |
|
|
| ```json |
| { |
| "hint": "gl_account", |
| "confidence": 0.6323356628417969 |
| } |
| ``` |
|
|
| The raw ONNX input is a single string tensor named `text`. MockGen normally builds this string |
| from property metadata, for example: |
|
|
| ```text |
| property_name: SAKNR [SEP] label: G/L Account [SEP] entity_type_name: JournalEntryItem [SEP] neighbor_properties: CompanyCode, FiscalYear, AmountInCompanyCodeCurrency [SEP] type: string |
| ``` |
|
|
| ## Training data |
|
|
| Training data came from reviewed schema-property rows built during the cross-family |
| classifier pipeline. The public dataset repository contains the publication-safe subset: |
| project-authored fixture rows, reviewed public API field metadata, and reviewed recovery |
| fixtures with source/provenance metadata. The exportable checkpoint uses a `word-sgd` |
| backend with `unicode-codepoint-v1` normalization rather than a pretrained encoder, because |
| this path produced a compact ONNX artifact with stable multilingual signal. |
|
|
| ## Evaluation |
|
|
| Local validation evidence: |
|
|
| - Training rows: 21,533; validation rows: 2,393. |
| - Full-precision validation top-1: 0.9820309235269536. |
| - Full-precision validation top-3: 0.9887170915169243. |
| - Quantized ONNX validation top-1: 0.9816130380275805. |
| - Quantized ONNX validation top-3: 0.9887170915169243. |
| - Absolute top-1 drop after quantization: 0.00041788549937316066, below the 0.01 gate. |
| - Quantized ONNX size: 5.101428985595703 MiB, below the 50 MiB gate. |
| - Quantized ONNX SHA-256: `b8827b79fc4def92ab6dbac7990a708771a8e72693df63c55f3b4f88d420ded9`. |
| - Classifier replacement gate: a new trained artifact must improve both the service-holdout |
| macro-F1 baseline `0.8247` and the domain-holdout macro-F1 baseline `0.7073`. |
| - Most recent experimental retraining status: not published because it did not improve both |
| benchmarks. |
|
|
| ## Limitations |
|
|
| The v1 vocabulary has about 50 trained hints with reserved slots for expansion. Expected weaknesses include rare enterprise fields, ambiguous labels without neighboring-property context, non-Western naming conventions, and schema domains not represented in the benchmark corpus. |
|
|
| ## Reproducibility |
|
|
| Use `models/classifier/training-config.yaml`, `models/classifier/metrics.json`, |
| `models/classifier/onnx/export-report.json`, and the paired dataset repository as the local |
| reproducibility anchors. Public artifact checksums are verified from the model repository |
| after publication. |
|
|
