52 locales, per-row slots (one example per slot, no cross-utterance aggregation), README dataset card

README.md

@@ -1,164 +1,181 @@
 ---
 pretty_name: MASSIVE Templates
+language:
+- af
+- am
+- ar
+- az
+- bn
+- ca
+- cy
+- da
+- de
+- el
+- en
+- es
+- fa
+- fi
+- fr
+- he
+- hi
+- hu
+- hy
+- id
+- is
+- it
+- ja
+- jv
+- ka
+- km
+- kn
+- ko
+- lv
+- ml
+- mn
+- ms
+- my
+- nb
+- nl
+- pl
+- pt
+- ro
+- ru
+- sl
+- sq
+- sv
+- sw
+- ta
+- te
+- th
+- tl
+- tr
+- ur
+- vi
+- zh
 license: apache-2.0
 task_categories:
-  - text-classification
+- text-classification
+- token-classification
 tags:
-  - intent-classification
-  - ovos
-  - multilingual
-language:
-  - af
-  - am
-  - ar
-  - az
-  - bn
-  - ca
-  - cy
-  - da
-  - de
-  - el
-  - en
-  - es
-  - fa
-  - fi
-  - fr
-  - he
-  - hi
-  - hu
-  - hy
-  - id
-  - is
-  - it
-  - ja
-  - jv
-  - ka
-  - km
-  - kn
-  - ko
-  - lv
-  - ml
-  - mn
-  - ms
-  - my
-  - nb
-  - nl
-  - pl
-  - pt
-  - ro
-  - ru
-  - sl
-  - sq
-  - sv
-  - sw
-  - ta
-  - te
-  - th
-  - tl
-  - tr
-  - ur
-  - vi
-  - zh
+- intent-classification
+- slot-filling
+- multilingual
+- ovos
+- massive
+size_categories:
+- 100K<n<1M
 ---
 
 # MASSIVE Templates
 
-Multilingual intent-classification dataset re-cast from
-[`AmazonScience/massive`](https://huggingface.co/datasets/AmazonScience/massive)
-(Apache-2.0) into the OVOS `intents-for-eval` shape: one row per template,
-with `{slot}` placeholders and per-slot vocab pools. Conversion is purely
-mechanical — a single regex pass over MASSIVE's `annot_utt` brackets, no LLM
-in the loop.
-
-This is an alternate view of an already-public dataset; the raw MASSIVE
-source is not redistributed here, only the converted templates.
-
-## Languages
+A mechanical re-cast of [`AmazonScience/massive`](https://huggingface.co/datasets/AmazonScience/massive)
+into the same row shape as [`OpenVoiceOS/intents-for-eval`](https://huggingface.co/datasets/OpenVoiceOS/intents-for-eval).
+Every MASSIVE utterance becomes one training row with a bracket template
+(`{slot}` placeholders) plus a list of slot examples drawn **only** from
+that single utterance. **No LLM** — the conversion is a single regex pass
+over MASSIVE's `annot_utt` field.
 
-52 BCP-47 locales (the full MASSIVE locale set):
+| | |
+|---|---|
+| **Source** | `AmazonScience/massive` (Apache-2.0) |
+| **Languages** | 52 BCP-47 locales |
+| **Train rows** | ~13.5k per locale (train + dev pooled) — ~704k total |
+| **Test rows** | 2,974 per locale — ~155k total |
+| **Schema fit** | Drop-in compatible with `OpenVoiceOS/intents-for-eval` consumers |
 
-`af-ZA, am-ET, ar-SA, az-AZ, bn-BD, ca-ES, cy-GB, da-DK, de-DE, el-GR, en-US,
-es-ES, fa-IR, fi-FI, fr-FR, he-IL, hi-IN, hu-HU, hy-AM, id-ID, is-IS, it-IT,
-ja-JP, jv-ID, ka-GE, km-KH, kn-IN, ko-KR, lv-LV, ml-IN, mn-MN, ms-MY, my-MM,
-nb-NO, nl-NL, pl-PL, pt-PT, ro-RO, ru-RU, sl-SL, sq-AL, sv-SE, sw-KE, ta-IN,
-te-IN, th-TH, tl-PH, tr-TR, ur-PK, vi-VN, zh-CN, zh-TW`
-
-## License
-
-Apache-2.0, inherited from MASSIVE.
-
-## Layout
-
-Each locale has its own directory with three JSONL files, matching the
-[`OpenVoiceOS/intents-for-eval`](https://huggingface.co/datasets/OpenVoiceOS/intents-for-eval)
-layout:
+## How the conversion works
 
+MASSIVE annotates utterances inline:
 ```
-<lang>/train_templates.jsonl   # one row per (intent, distinct slot-sequence)
-<lang>/train_keywords.jsonl    # one row per intent (full vocab pool)
-<lang>/test.jsonl              # one row per MASSIVE test utterance
+annot_utt = "wake me up at [time : five am] [date : this week]"
 ```
 
-### Schema
-
-`train_templates.jsonl`:
+A single regex `\[\s*([A-Za-z0-9_]+)\s*:\s*([^\[\]]+?)\s*\]` extracts each
+`[slot : value]` span; the span gets replaced with `{slot}` in the template
+and `(slot, value)` is recorded for the row's slot list:
 
 ```json
 {
   "intent_id": "alarm:alarm_set",
-  "domain": "alarm",
-  "template": "wake me up at {time} on {date}",
+  "domain":    "alarm",
+  "template":  "wake me up at {time} on {date}",
   "slots": [
-    {"name": "time", "examples": ["nine am", "five am", ...]},
-    {"name": "date", "examples": ["friday", "tomorrow", ...]}
+    {"name": "time", "examples": ["nine am"]},
+    {"name": "date", "examples": ["friday"]}
   ]
 }
 ```
 
-`train_keywords.jsonl`:
+The `examples` list carries **only** the values from that specific
+utterance — no cross-utterance aggregation. Two utterances with identical
+templates but different slot fills become two distinct rows.
+
+Test rows mirror `intents-for-eval`'s shape:
 
 ```json
 {
-  "intent_id": "alarm:alarm_set",
-  "domain": "alarm",
-  "optional_vocab": {"time": [...], "date": [...]}
+  "utterance":       "wake me up at five am",
+  "expected_intent": "alarm:alarm_set",
+  "expected_slots":  {"time": "five am"},
+  "split":           "test"
 }
 ```
 
-`test.jsonl`:
+## Repo layout
 
-```json
-{
-  "utterance": "wake me up at five am this week",
-  "expected_intent": "alarm:alarm_set",
-  "expected_slots": {"time": "five am", "date": "this week"},
-  "split": "test"
-}
+```
+<locale>/train_templates.jsonl
+<locale>/test.jsonl
 ```
 
-### Configs
+There is no `train_keywords.jsonl` because MASSIVE has no per-intent
+keyword pool to lift; engines that want one can derive it from the union
+of every template row's slot values at load time.
 
-Three configs per language follow the `intents-for-eval` convention:
+## Reproducing
 
-- `<lang>-templates`  → `train_templates.jsonl`
-- `<lang>-keywords`  → `train_keywords.jsonl`
-- `<lang>-test`      → `test.jsonl`
+The converter and a ready-to-use loader live in
+[`OpenVoiceOS/ovos-intent-benchmark`](https://github.com/OpenVoiceOS/ovos-intent-benchmark):
 
-## Generation recipe
+```bash
+git clone https://github.com/OpenVoiceOS/ovos-intent-benchmark
+cd ovos-intent-benchmark
+pip install -e .
+python scripts/convert_massive_to_templates.py --langs all --out data/massive_templates/
+```
+
+## Loading
 
-Run `scripts/convert_massive_to_templates.py --langs all --out data/massive_templates/`
-from [`OpenVoiceOS/ovos-intent-benchmark`](https://github.com/OpenVoiceOS/ovos-intent-benchmark).
+Via the benchmark loader (matches `IntentsForEvalDataset`):
+
+```python
+from benchmark.datasets import get
+ds = get("massive_templates")
+train, test = ds.load("en-US")
+```
+
+Or directly via `datasets`:
+
+```python
+from datasets import load_dataset
+templates = load_dataset("OpenVoiceOS/massive-templates", "en-US-templates", split="train")
+test = load_dataset("OpenVoiceOS/massive-templates", "en-US-test", split="test")
+```
+
+## License
+
+Apache-2.0, inherited from MASSIVE.
 
 ## Citation
 
-This dataset is a derivative re-packaging. Please cite MASSIVE:
+If you use this dataset, please cite the original MASSIVE paper:
 
 ```bibtex
-@misc{fitzgerald2022massive,
-  title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
-  author={FitzGerald, Jack and others},
-  year={2022},
-  eprint={2204.08582},
-  archivePrefix={arXiv}
+@article{fitzgerald2022massive,
+  title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset
+         with 51 Typologically-Diverse Languages},
+  author={FitzGerald, Jack and Hench, Christopher and Peris, Charith and others},
+  journal={arXiv preprint arXiv:2204.08582},
+  year={2022}
 }
 ```

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