| --- |
| library_name: premove-itn |
| language: |
| - en |
| base_model: |
| - microsoft/deberta-v3-large |
| tags: |
| - inverse-text-normalization |
| - text-normalization |
| - speech-processing |
| - voice-agents |
| - structured-prediction |
| - candidate-ranking |
| - safetensors |
| - custom-code |
| license: mit |
| --- |
| |
| # Premove ITN v0.1.0 |
|
|
| Premove ITN is an open-weight contextual inverse text normalization system for |
| English voice-agent transcripts. It turns spoken-form ASR text into structured |
| written text: |
|
|
| ```text |
| call me at four thirty → call me at 04:30 |
| the total is twenty dollars → the total is $20 |
| ``` |
|
|
| Deterministic Rust realizers propose valid written forms. A |
| DeBERTa-v3-large contextual scorer uses the complete sentence to score those |
| candidates, and an exact decoder selects compatible, non-overlapping edits. |
|
|
| This repository contains the frozen, inference-only v0.1.0 model artifact. It |
| does not contain optimizer state, scheduler state, training counters, training |
| data, or evaluation rows. Source code and retained evaluation evidence are in |
| [`premove-ai/premove-itn`](https://github.com/premove-ai/premove-itn). |
|
|
| ## Loading the model |
|
|
| This is a custom candidate-scoring architecture. Do not load it with |
| `AutoModel.from_pretrained()`. |
|
|
| ```python |
| from premove_itn import PremoveITN |
| |
| itn = PremoveITN.from_pretrained() |
| print(itn.normalize("call me at four thirty")) |
| # call me at 04:30 |
| ``` |
|
|
| The `premove-itn` PyPI release is not published yet. Until the public package |
| release, contributors can build and install the release wheel from the GitHub |
| repository. Create one `PremoveITN` instance and reuse it; model initialization |
| is expensive compared with warm normalization. |
|
|
| `device="auto"` selects CUDA when available, then Apple MPS, then CPU. The |
| current release candidate has been validated end-to-end only on macOS Apple |
| Silicon with Python 3.11 and MPS. Other environments require release |
| validation. |
|
|
| ## Architecture |
|
|
| ```text |
| Spoken ASR text |
| ↓ |
| deterministic Rust candidates |
| ↓ |
| DeBERTa-v3-large contextual scores |
| ↓ |
| exact maximum-score decoder |
| ↓ |
| written transcript |
| ``` |
|
|
| The scorer has 435,594,145 parameters. The artifact contains the complete |
| trained state in `model.safetensors`, the pinned DeBERTa configuration, and the |
| tokenizer files required by the release. |
|
|
| Supported candidate kinds are `DIGIT_SEQUENCE`, `CARDINAL`, `TIME`, `DATE`, |
| `MONEY`, `DECIMAL`, `PHONE`, `ELECTRONIC`, `MEASUREMENT`, `ORDINAL`, |
| `PUNCTUATION`, `WHITELIST`, and `WORD`. |
|
|
| ## First Evaluation |
|
|
| The retained First Evaluation used a frozen, balanced synthetic stress suite. |
| Semantic entity accuracy is the primary structured-value metric. Strict exact |
| match separately measures the complete canonical output. |
|
|
| ### Dedicated voice-agent rows |
|
|
| | Backend | Correct entities | Semantic accuracy | Mean latency | |
| | --- | ---: | ---: | ---: | |
| | **Premove ITN** | **398/400** | **99.50%** | 57.41 ms | |
| | Thutmose | 268/400 | 67.00% | 16.04 ms | |
| | text-processing-rs | 273/400 | 68.25% | 0.15 ms | |
|
|
| ### Overall 1,500-row benchmark |
|
|
| | Backend | Semantic accuracy | Strict exact | Mean latency | |
| | --- | ---: | ---: | ---: | |
| | **Premove ITN** | **89.70%** | **40.53%** | 56.49 ms | |
| | Thutmose | 59.39% | 22.13% | 15.98 ms | |
| | text-processing-rs | 55.79% | 16.53% | 0.14 ms | |
|
|
| Premove led measured semantic accuracy overall and on the 400 dedicated |
| voice-agent rows. It did not lead latency. |
|
|
| Latency used sequential batch-one requests on an Apple M4 MacBook Air with |
| MPS, an optimized Rust extension, and eight Rayon workers. Models were loaded |
| and warmed before request latency was measured. Download and initialization |
| are excluded. The result is not an estimate of production-traffic accuracy, |
| and the evaluation was not blind. |
|
|
| See the |
| [`First Evaluation report`](https://github.com/premove-ai/premove-itn/blob/main/eval/voice_agent_itn/results/first-evaluation/REPORT.md) |
| and |
| [`detailed tables`](https://github.com/premove-ai/premove-itn/blob/main/eval/voice_agent_itn/results/first-evaluation/DETAILS.md). |
|
|
| ## Intended use |
|
|
| Use Premove for English voice-agent transcripts in which numbers, dates, |
| times, money, phone values, identifiers, URLs, and related structured values |
| need sentence-level disambiguation. The runtime receives only transcript text. |
| Candidate metadata is generated internally. |
|
|
| ## Model lifecycle |
|
|
| - The first use downloads about 1.6 GB; duration depends on the network. |
| - Cached initialization takes several seconds on the tested system. |
| - Warm normalization averaged 56.49 ms in the retained MPS benchmark. |
| - Services and transcript streams should keep one normalizer resident. |
|
|
| ## Limitations |
|
|
| - English only. |
| - A 435.6M-parameter model with an approximately 1.6 GB download. |
| - Multi-second initialization. |
| - Not compatible with generic `AutoModel.from_pretrained()` loading. |
| - The benchmark is synthetic and does not measure live production traffic. |
| - Weaker measured categories include URL, MONEY, CARDINAL, TIME, |
| REFERENCE_ID, and VERSION. |
| - Blind human gold adjudication and broader contamination checks remain |
| incomplete. |
| - End-to-end release validation currently covers macOS Apple Silicon, Python |
| 3.11, and MPS only. |
| |
| ## Release identity and provenance |
| |
| - Artifact version: `v0.1.0` |
| - Architecture: `premove-candidate-scorer-v1` |
| - Required package version: `0.1.0` |
| - Hub repository: `premove-ai/premove-itn` |
| - Immutable model commit: `80bda5e2e1fe9542aa628597090242df57c1a157` |
| - Base model: `microsoft/deberta-v3-large` |
| - Base model revision: `64a8c8eab3e352a784c658aef62be1662607476f` |
| - Model SHA-256: `119c0f19767b61446e04da1f8f01a001edf97a47a66965e7146db2483b4937a1` |
| |
| The package pins the immutable model commit and verifies its release metadata, |
| base-model identity, and model digest before inference. Full training |
| composition and checkpoint selection evidence are in the |
| [`production model record`](https://github.com/premove-ai/premove-itn/blob/main/docs/model-provenance.md). |
| |
| ## License and attribution |
| |
| Premove ITN source code and model weights are MIT licensed. The scorer uses |
| [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large) |
| at the revision above. Its architecture derives from the |
| [`DeBERTaV3` paper](https://arxiv.org/abs/2111.09543). |
| |
| The Rust realization layer uses |
| [`text-processing-rs`](https://github.com/FluidInference/text-processing-rs), |
| which is Apache-2.0 licensed. Required notices are retained in the source |
| repository. |
| |
