---
library_name: premove-itn
language: [en]
base_model: [microsoft/deberta-v3-large]
tags: [inverse-text-normalization, speech-processing, voice-agents]
license: mit
---

# Premove ITN v0.2.0

This release updates the contextual candidate scorer used by `premove-itn`.
The deterministic Rust candidate generators and exact decoder are unchanged.

Load it through the matching Python package:

```python
from premove_itn import PremoveITN

itn = PremoveITN.from_pretrained(revision="v0.2.0")
print(itn.normalize("meet me at two thirty"))
# meet me at 02:30
```

## Training

The v0.1.0 scorer was adapted for three epochs on 7,440 controlled,
single-collision TIME-versus-identifier records. No replay or dual-collision
examples were used. Epoch 3 was selected using a separate 200-row development
set before any frozen test was inspected.

## Results

| Metric | v0.1.0 | v0.2.0 |
| --- | ---: | ---: |
| Context record | 51.2% | **86.2%** |
| Counterfactual pair | 6.4% | **72.4%** |
| Identifier | 29.6% | **98.0%** |
| Time | 72.8% | **74.4%** |
| Dual span | 45.0% | **96.0%** |
| Dual sentence exact | 16.0% | **92.0%** |
| Broad strict exact | 40.5% | **43.7%** |

The contextual and dual benchmarks are synthetic controlled evaluations. They
do not estimate production voice-agent accuracy. The broad benchmark gained 86
new exact rows and lost 38 previously exact rows. Localized losses were most
visible in ORDINAL, MONEY, URL, and DIGIT_SEQUENCE.

## Limitations

- TIME recall on the controlled single-collision test is 74.4%, substantially
  below the 98.0% identifier result.
- The model is English-only and requires the `premove-itn` candidate graph and
  decoder. It is not a generic Transformers model.
- The approximately 435.6M-parameter scorer has a large download and
  multi-second initialization cost.

## Release identity

- Artifact version: `v0.2.0`
- Required package version: `0.2.0`
- Hub repository: `premove-ai/premove-itn`
- Base model: `microsoft/deberta-v3-large`
- Base revision: `64a8c8eab3e352a784c658aef62be1662607476f`
- Source checkpoint SHA-256: `10a338d57b6d619f52259a58aca210ecb78f47de44baf0c25477a8f5b06b5d39`
- Model SHA-256: `0b6f36aa32311d0c495e1c5307d5e34463e52b4dc283ab9030bc2f54e1bf1152`

Source and evaluation evidence are available from
[`premove-ai/premove-itn`](https://github.com/premove-ai/premove-itn).