Card: usage for omni-parse-bench 1.0.0

README.md

@@ -40,8 +40,7 @@ is built to answer the question you actually have: which parser is best on docum
 It has 16,288 tests on 2,937 pages from 2,343 documents over 22 suites, in
 92 languages. Omni Parse Bench is developed by Datalab.
 
-Read more in our [blog post](https://www.datalab.to/blog/omni-parse-bench). Also see the [GitHub](https://github.com/datalab-to/omni_parse_bench) for the scorer and the harness that
-runs parsers on the benchmark.
+Read more in our [blog post](https://www.datalab.to/blog/omni-parse-bench). **Also see the [GitHub](https://github.com/datalab-to/omni_parse_bench) for how to run the benchmark and use the scorer.**
 
 ## The design
 
@@ -190,138 +189,15 @@ An arg is `{"text", "max_diffs"}`, `{"number"}` or `{"latex"}`, and its kind say
 compared: text within `max_diffs` edits, a number digit for digit, LaTeX by what it renders (or by its plain
 text, when plain text writes it without loss).
 
-## Install
+## Use
 
 ```bash
-uv pip install omni-parse-bench                 # the scorer: score(), no network, no API keys
-uv pip install 'omni-parse-bench[harness]'      # + vendor adapters, to predict
-uv pip install 'omni-parse-bench[benchmark]'    # + dataset access, to run the benchmark
+uv pip install 'omni-parse-bench[benchmark]'
 ```
 
-This revision of the dataset needs omni-parse-bench 0.1.2 or newer. With 0.1.1, use the revision made for
-it: `--dataset hf://datasets/datalab-to/omni_parse_bench@1c35a52`.
-
-## Run the benchmark
-
-With `[benchmark]` installed, run parsers on the dataset (5 pages here). It shows the plan and asks before
-calling any vendor, since calls cost money; each vendor needs its API key in the environment. A run is
-resumable: stop it, and run the same command again to carry on.
-
-```bash
-opb benchmark --out runs/ --limit 5 --providers datalab openai/gpt-5.6-sol
-```
-
-Each setting of a provider is its own run; here, two:
-
-```bash
-opb benchmark --out runs/ --limit 5 --providers datalab   --options '{"datalab": [{"mode": "balanced"}, {"mode": "accurate"}]}'
-```
-
-`--dataset` picks the dataset: a folder, or `hf://datasets/datalab-to/omni_parse_bench@<revision>` (this one, at `main`, by
-default). Corrections land on `main` as new commits; pin a revision to reproduce a result. Each run's
-`scores.jsonl` has one line per test, with what the test is beside its verdict, so a run can be compared by
-tag, suite or test type: see [`docs/API.md`](https://github.com/datalab-to/omni_parse_bench/blob/main/docs/API.md#asking-a-question).
-
-## Score
-
-Use in your own code: one page with three tests, written inline so the gold file's shape is visible
-(`gold.read("gold/<page_id>.json")` reads a file).
-
-```python
-from omni_parse_bench import gold, score, summarize
-
-tests = gold.parse("example-p1", {"tests": [
-    {
-        "id": "t1",
-        "test_type": "present",
-        "output_type": "html",
-        "derivation": "text_layer",
-        "tags": ["Latn", "eng"],
-        "args": {"content": {"text": "Quarterly revenue rose 12%", "max_diffs": 1}},
-        "settings": {},
-    },
-    {
-        "id": "t2",
-        "test_type": "order",
-        "output_type": "html",
-        "derivation": "text_layer",
-        "tags": ["Latn", "eng", "heading"],
-        "args": {
-            "before": {"text": "Summary", "max_diffs": 0},
-            "after": {"text": "Outlook", "max_diffs": 0},
-        },
-        "settings": {},
-    },
-    {
-        "id": "t3",
-        "test_type": "table_cell",
-        "output_type": "html",
-        "derivation": "publisher_markup",
-        "tags": ["table"],
-        "args": {
-            "cell": {"text": "4.2", "max_diffs": 0},
-            "top_heading": {"text": "Q3", "max_diffs": 0},
-        },
-        "settings": {},
-    },
-]})
-
-outputs = {
-    "html": (
-        "<h2>Summary</h2>"
-        "<p>Quarterly revenue rose 12% on the year.</p>"
-        "<table>"
-        "<tr><th>Q2</th><th>Q3</th></tr>"
-        "<tr><td>3.9</td><td>4.1</td></tr>"
-        "</table>"
-        "<h2>Outlook</h2>"
-    ),
-}
-verdicts = score(outputs, tests)
-```
-
-```python
->>> verdicts
-[Verdict(id='t1', result='pass', why=''),
- Verdict(id='t2', result='pass', why=''),
- Verdict(id='t3', result='fail', why="no cell matching '4.2' in any table")]
-
->>> s = summarize(verdicts, tests)["html"]
->>> s["score"], s["per_test_type"], s["per_tag"]["table"]
-(0.6666666666666666, {'present': 1.0, 'order': 1.0, 'table_cell': 0.0}, {'score': 0.0, 'tests': 1, 'pages': 1})
-```
-
-A verdict is `pass`, `fail`, `unsupported` (the parser can't produce that output) or `error` (grading
-raised: a bug in the scorer, not the parser's fault), with `why` saying what went wrong. `unsupported`
-and `error` verdicts are left out of every score.
-
-From the command line, with the outputs in a JSON file:
-
-```bash
-opb score --pred outputs.json --gold gold/<page_id>.json
-```
-
-## Predict
-
-With `[harness]` installed, predict with our provider adapters:
-
-```python
-from omni_parse_bench import harness
-
-record = harness.predict("datalab", "pages/<page_id>.pdf", {"html", "blocks"}, mode="balanced")
-record["outputs"]       # {"html": "...", "blocks": [...]}
-record["errors"]        # {output: why} for anything that failed
-record["calls"]         # what was sent, cost, wall time
-
-score(record["outputs"], gold.read("gold/<page_id>.json"))
-```
-
-From the command line:
-
-```bash
-opb predict --provider openai/gpt-5.6-sol --page pages/<page_id>.pdf --wants html
-opb providers        # every provider, and its options
-```
+How to run the benchmark on this dataset, grade a run, score a page on its own, and use the adapters: the
+[omni-parse-bench README on GitHub](https://github.com/datalab-to/omni_parse_bench). This dataset needs
+omni-parse-bench 1.0.0 or newer.
 
 ## How the tests were built