Datasets:

hadro
/

cde-evals

---
license: other
license_name: mixed-see-provenance
language:
  - en
  - fr
tags:
  - glam
  - city-directory
  - historical-documents
  - structured-extraction
  - evaluation
pretty_name: City Directory Extraction — Gold Evaluation Panel
size_categories:
  - 1K<n<10K
viewer: false
---
# City Directory Extraction — Gold Evaluation Sets

**Evaluation only. Never train on these.** This is the regression harness for
[`hadro/city-directory-extraction`](https://github.com/hadro/city-directory-extraction) —
the gold sets used to score every fine-tune of the Qwen3.5 city-directory extractor.

> **Why this repo is private.** Two reasons, and the second is the stronger one:
> 1. `nyu_eval.jsonl` is **CC-BY-SA-NC 4.0** (non-commercial + share-alike).
> 2. Public gold sets get scraped into training corpora. If this panel leaks into a
>    pretraining crawl, it stops measuring anything. Keep it out of public mirrors,
>    public HF Spaces, and pasted-into-a-chat contexts.
## Schema
Every file is JSONL, one directory entry per line:
```json
{
  "raw_line": "Brower N. merchant, 95, Water-street",
  "context": {"publisher": "franks", "alphabetical_range": "", "directory_year": "1786", "image": "..."},
  "record": {
    "name": "Brower N.", "is_business": false, "spouse_name": "", "race_designation": "",
    "occupation_role": "merchant", "employer": "", "address": "95 Water-street", "home_address": ""
  }
}
```
- `raw_line` — the entry as printed (model input).
- `context` — page-level metadata fed in the prompt, **not** predicted.
- `record` — the 8-field target. `context.image` is present on the panel volumes only.

## The 18-volume NYC panel — the regression harness

Hand-labeled by this project from library page scans (NYPL / Internet Archive / LoC),
one gold set per volume, all validator-clean. NYC 1786–1933 across boroughs and publishers.
**1169 lines total.** This is the panel, not a sample — score all 18.

| File | Rows |
|---|---|
| `franks1786_eval.jsonl` | 56 |
| `duncan1794_eval.jsonl` | 58 |
| `mercein1820_eval.jsonl` | 60 |
| `ogden1839_eval.jsonl` | 66 |
| `doggett1846_eval.jsonl` | 37 |
| `rode1851_eval.jsonl` | 53 |
| `hearne1852_eval.jsonl` | 52 |
| `hopehenderson1856_eval.jsonl` | 60 |
| `lain1876_eval.jsonl` | 103 |
| `boyd1890_eval.jsonl` | 75 |
| `trow1907_eval.jsonl` | 68 |
| `trow1913_eval.jsonl` | 93 |
| `polk1917_eval.jsonl` | 72 |
| `polk1925_eval.jsonl` | 40 |
| `mb1931_eval.jsonl` | 109 |
| `polk1933bk_eval.jsonl` | 49 |
| `polk1933si_eval.jsonl` | 56 |
| `queens1933_eval.jsonl` | 62 |

## Externals + controls

| File | Rows | Source | License | Notes |
|---|---|---|---|---|
| `synth_dev.jsonl` | 1,000 | generated by `synth_persons.py` | ours (CC-BY-4.0) | In-distribution control. Near-ceiling scores are expected; it detects breakage, not quality. |
| `nyu_eval.jsonl` | 3,000 | [NYU NYC directories 1850–1890](https://archive.nyu.edu/handle/2451/61521) | **CC-BY-SA-NC 4.0** ⚠ | **Score at `--limit 500`** — every published baseline is on the first 500 rows. |
| `ftd_eval.jsonl` | 2,000 | [French Trade Directories (SODUCO)](https://zenodo.org/records/8167628) | open (CC) | Cross-lingual transfer eval. |
| `tulsa_eval.jsonl` | 1,500 | own `directory-pipeline` output (Tulsa 1921) | ours | Real OCR + Gemini-extracted (**silver**) labels, not hand-verified. |
| `lain_eval.jsonl` | 800 | own pipeline output (Lain Brooklyn 1897) | ours | Silver labels. Held out of the sampling/harvest set. |
| `minneapolis_eval.jsonl` | 230 | [DirCity](https://github.com/adamrangwala/DirCity_Directory_Crop-out-with-Key-Lines) | MIT | Out-of-city US eval (silver). |

Note `lain_eval.jsonl` (silver, 800 rows, full volume) and `lain1876_eval.jsonl`
(gold, 103 rows, panel volume) are **different sets** — don't merge them.

## How to run it

The harness lives in the public repo; only the data is here.

```bash
git clone https://github.com/hadro/city-directory-extraction
cd city-directory-extraction
hf download hadro/cde-evals --repo-type dataset --local-dir data/
```

Then, on the cluster:

```bash
source hpc/env.sh
sbatch $(slurm_gpu_args) --export=ALL,RUN_NAME=<run> hpc/30_eval.sbatch
```

`hpc/30_eval.sbatch` runs the 18 panel volumes, then the externals, then
`eval/evaluate.py` and the `eval/results_table.py` pivot. It sets `HF_HUB_OFFLINE=1`,
so **download this dataset on the login node first** — compute nodes have no network.

Two gotchas that have each cost a full eval cycle:

- **Pass `--base-model`.** `train/sft_qwen.py` trains LoRA, so the run directory is an
  adapter, not a full checkpoint.
- **Watch the log for `missing adapter keys`.** That warning means the adapter silently
  did not apply and you scored the bare base model. `qwen_predict.py` must load
  `AutoModelForImageTextToText` (not `AutoModelForCausalLM`) — Qwen3.5 is multimodal and
  `all-linear` adapts the vision tower too. This bug produced a 0.358 macro-F1 that was
  really 0.760.

Scoring: `macro-F1` averages over the fields the gold actually has; `micro-F1` is
frequency-weighted. They disagree, and both are reported. See `eval/evaluate.py`.

## Provenance and redistribution

Mixed licensing — there is no single license covering this repo:

- **The 18-volume panel** is this project's own hand labeling of public-domain page scans.
- **`nyu_eval.jsonl` is CC-BY-SA-NC 4.0.** Non-commercial and share-alike. It is used for
  *evaluation only* and is never mixed into training, which is what keeps the released
  synthetic-trained model permissively reusable.
- **`ftd_eval.jsonl`** is open CC from the SODUCO project.
- **`minneapolis_eval.jsonl`** derives from an MIT-licensed repo.
- **`tulsa`/`lain`** are this project's own pipeline output.
Check the terms before redistributing any of it. The silver-labeled sets
(`tulsa`, `lain`, `minneapolis`) are machine-extracted and not hand-verified — treat their
absolute numbers as approximate and use them for relative comparison across runs.
## Related
- [`hadro/city-directory-extraction`](https://github.com/hadro/city-directory-extraction) — code, harness, docs
- [`hadro/city-directory-synth`](https://huggingface.co/datasets/hadro/city-directory-synth) — synthetic **training** data (public)
- `hadro/city-dir-08b-yaml-v5` — current best adapter

	---
	license: other
	license_name: mixed-see-provenance
	language:
	- en
	- fr
	tags:
	- glam
	- city-directory
	- historical-documents
	- structured-extraction
	- evaluation
	pretty_name: City Directory Extraction — Gold Evaluation Panel
	size_categories:
	- 1K<n<10K
	viewer: false
	---

	# City Directory Extraction — Gold Evaluation Sets

	Evaluation only. Never train on these. This is the regression harness for
	[`hadro/city-directory-extraction`](https://github.com/hadro/city-directory-extraction) —
	the gold sets used to score every fine-tune of the Qwen3.5 city-directory extractor.

	> Why this repo is private. Two reasons, and the second is the stronger one:
	> 1. `nyu_eval.jsonl` is CC-BY-SA-NC 4.0 (non-commercial + share-alike).
	> 2. Public gold sets get scraped into training corpora. If this panel leaks into a
	> pretraining crawl, it stops measuring anything. Keep it out of public mirrors,
	> public HF Spaces, and pasted-into-a-chat contexts.

	## Schema

	Every file is JSONL, one directory entry per line:

	```json
	{
	"raw_line": "Brower N. merchant, 95, Water-street",
	"context": {"publisher": "franks", "alphabetical_range": "", "directory_year": "1786", "image": "..."},
	"record": {
	"name": "Brower N.", "is_business": false, "spouse_name": "", "race_designation": "",
	"occupation_role": "merchant", "employer": "", "address": "95 Water-street", "home_address": ""
	}
	}
	```

	- `raw_line` — the entry as printed (model input).
	- `context` — page-level metadata fed in the prompt, not predicted.
	- `record` — the 8-field target. `context.image` is present on the panel volumes only.

	## The 18-volume NYC panel — the regression harness

	Hand-labeled by this project from library page scans (NYPL / Internet Archive / LoC),
	one gold set per volume, all validator-clean. NYC 1786–1933 across boroughs and publishers.
	1169 lines total. This is the panel, not a sample — score all 18.

	\| File \| Rows \|
	\|---\|---\|
	\| `franks1786_eval.jsonl` \| 56 \|
	\| `duncan1794_eval.jsonl` \| 58 \|
	\| `mercein1820_eval.jsonl` \| 60 \|
	\| `ogden1839_eval.jsonl` \| 66 \|
	\| `doggett1846_eval.jsonl` \| 37 \|
	\| `rode1851_eval.jsonl` \| 53 \|
	\| `hearne1852_eval.jsonl` \| 52 \|
	\| `hopehenderson1856_eval.jsonl` \| 60 \|
	\| `lain1876_eval.jsonl` \| 103 \|
	\| `boyd1890_eval.jsonl` \| 75 \|
	\| `trow1907_eval.jsonl` \| 68 \|
	\| `trow1913_eval.jsonl` \| 93 \|
	\| `polk1917_eval.jsonl` \| 72 \|
	\| `polk1925_eval.jsonl` \| 40 \|
	\| `mb1931_eval.jsonl` \| 109 \|
	\| `polk1933bk_eval.jsonl` \| 49 \|
	\| `polk1933si_eval.jsonl` \| 56 \|
	\| `queens1933_eval.jsonl` \| 62 \|

	## Externals + controls

	\| File \| Rows \| Source \| License \| Notes \|
	\|---\|---\|---\|---\|---\|
	\| `synth_dev.jsonl` \| 1,000 \| generated by `synth_persons.py` \| ours (CC-BY-4.0) \| In-distribution control. Near-ceiling scores are expected; it detects breakage, not quality. \|
	\| `nyu_eval.jsonl` \| 3,000 \| [NYU NYC directories 1850–1890](https://archive.nyu.edu/handle/2451/61521) \| CC-BY-SA-NC 4.0 ⚠ \| Score at `--limit 500` — every published baseline is on the first 500 rows. \|
	\| `ftd_eval.jsonl` \| 2,000 \| [French Trade Directories (SODUCO)](https://zenodo.org/records/8167628) \| open (CC) \| Cross-lingual transfer eval. \|
	\| `tulsa_eval.jsonl` \| 1,500 \| own `directory-pipeline` output (Tulsa 1921) \| ours \| Real OCR + Gemini-extracted (silver) labels, not hand-verified. \|
	\| `lain_eval.jsonl` \| 800 \| own pipeline output (Lain Brooklyn 1897) \| ours \| Silver labels. Held out of the sampling/harvest set. \|
	\| `minneapolis_eval.jsonl` \| 230 \| [DirCity](https://github.com/adamrangwala/DirCity_Directory_Crop-out-with-Key-Lines) \| MIT \| Out-of-city US eval (silver). \|

	Note `lain_eval.jsonl` (silver, 800 rows, full volume) and `lain1876_eval.jsonl`
	(gold, 103 rows, panel volume) are different sets — don't merge them.

	## How to run it

	The harness lives in the public repo; only the data is here.

	```bash
	git clone https://github.com/hadro/city-directory-extraction
	cd city-directory-extraction
	hf download hadro/cde-evals --repo-type dataset --local-dir data/
	```

	Then, on the cluster:

	```bash
	source hpc/env.sh
	sbatch $(slurm_gpu_args) --export=ALL,RUN_NAME=<run> hpc/30_eval.sbatch
	```

	`hpc/30_eval.sbatch` runs the 18 panel volumes, then the externals, then
	`eval/evaluate.py` and the `eval/results_table.py` pivot. It sets `HF_HUB_OFFLINE=1`,
	so download this dataset on the login node first — compute nodes have no network.

	Two gotchas that have each cost a full eval cycle:

	- Pass `--base-model`. `train/sft_qwen.py` trains LoRA, so the run directory is an
	adapter, not a full checkpoint.
	- Watch the log for `missing adapter keys`. That warning means the adapter silently
	did not apply and you scored the bare base model. `qwen_predict.py` must load
	`AutoModelForImageTextToText` (not `AutoModelForCausalLM`) — Qwen3.5 is multimodal and
	`all-linear` adapts the vision tower too. This bug produced a 0.358 macro-F1 that was
	really 0.760.

	Scoring: `macro-F1` averages over the fields the gold actually has; `micro-F1` is
	frequency-weighted. They disagree, and both are reported. See `eval/evaluate.py`.

	## Provenance and redistribution

	Mixed licensing — there is no single license covering this repo:

	- The 18-volume panel is this project's own hand labeling of public-domain page scans.
	- `nyu_eval.jsonl` is CC-BY-SA-NC 4.0. Non-commercial and share-alike. It is used for
	evaluation only and is never mixed into training, which is what keeps the released
	synthetic-trained model permissively reusable.
	- `ftd_eval.jsonl` is open CC from the SODUCO project.
	- `minneapolis_eval.jsonl` derives from an MIT-licensed repo.
	- `tulsa`/`lain` are this project's own pipeline output.

	Check the terms before redistributing any of it. The silver-labeled sets
	(`tulsa`, `lain`, `minneapolis`) are machine-extracted and not hand-verified — treat their
	absolute numbers as approximate and use them for relative comparison across runs.

	## Related

	- [`hadro/city-directory-extraction`](https://github.com/hadro/city-directory-extraction) — code, harness, docs
	- [`hadro/city-directory-synth`](https://huggingface.co/datasets/hadro/city-directory-synth) — synthetic training data (public)
	- `hadro/city-dir-08b-yaml-v5` — current best adapter