Add README

README.md

@@ -1,3 +1,101 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+  - en
+task_categories:
+  - visual-question-answering
+  - automatic-speech-recognition
+  - text-generation
+tags:
+  - evaluation
+  - benchmark
+  - multimodal
+  - edge-inference
+  - on-device
+  - litert-lm
+  - image
+  - audio
+  - text
+  - multi-turn
+size_categories:
+  - n<1K
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: data/test/metadata.jsonl
+pretty_name: EdgeMMEval
+---
+
+# EdgeMMEval
+
+Minimal multimodal evaluation dataset for on-device inference testing.
+Covers functional correctness, accuracy, latency stress, and memory
+pressure across image, audio, text, multi-turn, combination, structured
+output, and tool-calling cases.
+
+## Dataset summary
+
+The test split is defined in `data/test/metadata.jsonl` (**200** rows). Each
+row has a `test_id` (for example `IMG-001`, `STO-020`) and a `modality`.
+
+| Modality             | Samples | Focus |
+|----------------------|--------:|--------|
+| Image                | 34      | VQA, OCR, description, classification, resolution / loop stress |
+| Audio                | 33      | Transcription, spoken QA, translation, noise and edge cases |
+| Text                 | 39      | QA, translation, summarization, reasoning-style prompts |
+| Multi-turn           | 24      | Context retention, KV-cache stress |
+| Combination          | 28      | Cross-modal alignment |
+| Structured output    | 30      | JSON schema, regex, grammar-style constraints (`constraint_type`) |
+| Tool call            | 12      | Correct tool name, arguments, or valid refusal text |
+| **Total**            | **200** | |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("CortexSwarm/EdgeMMEval", split="test")
+print(ds[0])
+```
+
+## Scoring
+
+Each sample includes a `reference` field and usually `reference_variants` for
+automatic scoring. The scorer lives in this repo at `scripts/score.py`.
+
+**Pipeline**
+
+1. Run your model on each `test_id` and collect the model’s output string.
+2. Write a JSON object to **`results.json`** at the repo root: keys are
+   `test_id` values, values are the raw model outputs (strings).
+3. Run `python scripts/score.py`. It reads `data/test/metadata.jsonl` and
+   `results.json`, then writes **`report.json`**.
+
+**Metrics and pass rules** (see constants at the top of `score.py`)
+
+- Most tasks: **BLEU-1** vs `reference_variants`; pass if score **≥ 0.5**.
+- Summarization-style tasks (`task` in `summarization` / `summarize`): **ROUGE-L**;
+  pass if **≥ 0.4**.
+- **Structured output**: format check (JSON Schema, regex, or grammar-style
+  heuristic) plus content BLEU; pass if format is valid and BLEU **≥ 0.3**.
+- **Tool call**: compares expected tool/args or valid text-only refusal;
+  separate logic in `score_tool_call`.
+
+**`report.json` shape**
+
+- `summary`: `total_scored`, `total_passed`, `overall_avg`, `pass_rate_pct`,
+  `verdict` (`✓ INFERENCE WORKING` if pass rate ≥ 80%, else
+  `✗ ISSUES DETECTED`), and `skipped` (test IDs with **no** entry in
+  `results.json`).
+- `by_modality`: average score and pass counts per modality (empty if nothing
+  was scored).
+- `samples`: per-test rows with scores, metrics, and pass/fail.
+
+If **`total_scored` is 0**, every test ID was skipped—typically **`results.json`
+is missing or does not map test IDs to outputs**. Fix the results file and
+re-run the scorer.
+
+## License
+
+CC BY 4.0 — free to use with attribution.