# US-LEX-1 (/concepts/model)

Fieldgeist is an AI lab building tools for real estate. Verify, its first tool, is built around **US-LEX-1**, a purpose-built neural model that Fieldgeist trains on real US addresses. The model reads a line of free text and decides what each part of it is: the house number, the street, the unit, the city. The [parse endpoint](/api-reference/parse) is how you send it addresses today.

## How it reads an address [#how-it-reads-an-address]

<Steps>

<Step>

### Split [#split]

The line is split into words and punctuation, keeping what you typed.

</Step>

<Step>

### Label [#label]

The model gives every word one of 19 labels taken from USPS Publication 28. It reads the whole line at once, so context decides each label: in `350 5th ave apt 21b`, the words around `5th` make it the street name rather than a house number, and `21b` after `apt` is a unit number.

</Step>

<Step>

### Assemble [#assemble]

The labeled words become components, standardized to Publication 28 abbreviations (`ave` becomes `AVE`) and rendered into delivery lines. This step follows fixed rules, so the same labels always produce the same address.

</Step>

<Step>

### Score [#score]

`confidence` is the model's average certainty about the labels it gave. See [Confidence and warnings](/concepts/confidence-and-warnings).

</Step>

</Steps>

## The labels [#the-labels]

Each label becomes a component in the parse response.

| Label | Component | Example |
| --- | --- | --- |
| Address number | `addressNumber` | `350` |
| Address number suffix | `addressNumberSuffix` | `1/2` |
| Predirectional | `predirectional` | `W` |
| Street name | `streetName` | `5TH` |
| Street suffix | `streetSuffix` | `AVE` |
| Postdirectional | `postdirectional` | `NW` |
| Occupancy type | `occupancyType` | `APT` |
| Occupancy ID | `occupancyId` | `21B` |
| PO box type | `poBoxType` | `PO BOX` |
| PO box ID | `poBoxId` | `1234` |
| Rural route type | `ruralRouteType` | `RR` |
| Rural route ID | `ruralRouteId` | `2 BOX 15` |
| Firm | `firm` | A business name in the line |
| City | `city` | `NEW YORK` |
| State | `state` | `NY` |
| ZIP Code | `zip5` | `10118` |
| ZIP+4 | `zip4` | `0003` |
| Urbanization | `urbanization` | A Puerto Rico urbanization |
| Other | `other` | Words the model couldn't place |

## Where it runs [#where-it-runs]

The model runs at the edge, inside the request that asks for it. There is no separate model service or queue: every parse is read by the model before the response is sent.

## When the model doesn't answer [#when-the-model-doesnt-answer]

If the model can't run, or its labels contradict each other, a rule-based parser answers instead, and the response says so: `model` names `us-lex-rules` with `backend: "heuristic"` and a warning (`model_unavailable` or `model_output_invalid`) gives the reason. The rule-based parser is less reliable on unusual lines, so treat its answers as you would a low confidence.

## Versions [#versions]

Every parse response names the model that read the line. In the API, US-LEX-1 is `us-lex-1`:

```json
"model": { "name": "us-lex-1", "version": "0.1.0", "backend": "model" }
```

Fieldgeist trains new versions of US-LEX-1 and releases them to the API. Each release is listed in the [changelog](/changelog), tagged **Model**. Responses keep the same fields from one version to the next; what can change is how a line is read. If you store parses, store `model.version` with them so you know which reading you have.

## How the model improves [#how-the-model-improves]

Fieldgeist retrains the model on new address data. Misreads found in evaluation are reviewed by people before each training round.

If Verify misreads an address, email support@fieldgeist.com with the request's `x-request-id` header (see [Request IDs](/api-reference/errors#request-ids)) and what you expected.

## What the model doesn't do [#what-the-model-doesnt-do]

- **It doesn't correct spelling.** It labels what you sent. Near-miss street names are handled by the [lookup](/api-reference/lookup), which flags them with `fuzzy: true`.
- **It doesn't check that an address exists.** A well-formed address that doesn't exist can parse with high confidence. To match an address to a real parcel, use the lookup, which checks it against a parcel index Fieldgeist builds from public property records.