Simplify README and add 180s/gt10min viewer configs

README.md

@@ -22,6 +22,14 @@ configs:
     data_files:
       - split: preview
         path: viewer/caption_60s.parquet
+  - config_name: viewer_caption_180s
+    data_files:
+      - split: preview
+        path: viewer/caption_180s.parquet
+  - config_name: viewer_caption_gt10min
+    data_files:
+      - split: preview
+        path: viewer/caption_gt10min.parquet
   - config_name: viewer_spatial
     data_files:
       - split: preview
@@ -39,58 +47,18 @@ Training data for the LLaVA-OneVision-2 multimodal model family, covering large-
 | `mid_training_video/60s_rest/` | WebDataset (`.tar`) | 10,809 shards of ~60s video clips |
 | `mid_training_video/caption_v0/split_30s.jsonl` | JSONL | Captions for 30-second video clips |
 | `mid_training_video/caption_v0/split_60s.jsonl` | JSONL | Captions for 60-second video clips |
+| `mid_training_video/caption_v0/split_180s.jsonl` | JSONL | Captions for 180-second video clips |
+| `mid_training_video/caption_v0/split_gt10min.jsonl` | JSONL | Captions for >10-minute video clips |
 | `spatial/` | WebDataset (`.tar`) | 84 shards of spatial reasoning data (refcoco, visual genome, pointing, 3D, etc.) |
 
 ## Preview Configs
 
-The three `viewer_*` configs above expose small Parquet samples so the Hugging Face Dataset Viewer can render the data directly in the browser:
+The `viewer_*` configs above expose small Parquet samples so the Hugging Face Dataset Viewer can render the data directly in the browser:
 
 - **`viewer_caption_30s`** — 5 caption samples from 30-second clips
 - **`viewer_caption_60s`** — 5 caption samples from 60-second clips
+- **`viewer_caption_180s`** — 3 caption samples from 180-second clips
+- **`viewer_caption_gt10min`** — 1 caption sample from >10-minute clips
 - **`viewer_spatial`** — 10 spatial-reasoning samples with embedded thumbnail images, mixed across tasks (refcoco, visual genome, pointing, ca1m, osd, crosspoint, erqa, roborefer)
 
 These previews are intended for **schema inspection only**. For training, use the full `mid_training_video/` and `spatial/` shards.
-
-## Loading Examples
-
-### Stream the full spatial WebDataset
-```python
-from datasets import load_dataset
-
-ds = load_dataset(
-    "mvp-lab/LLaVA-OneVision-2-Data",
-    data_files="spatial/train_*.tar",
-    split="train",
-    streaming=True,
-)
-for sample in ds:
-    print(sample)
-    break
-```
-
-### Load a viewer preview
-```python
-from datasets import load_dataset
-
-ds = load_dataset("mvp-lab/LLaVA-OneVision-2-Data", "viewer_spatial", split="preview")
-print(ds[0])
-```
-
-## Schema
-
-### Caption (`viewer_caption_30s`, `viewer_caption_60s`)
-- `images_source: List[str]` — relative paths to source video files
-- `messages: List[{role, content}]` — chat-format caption prompt and assistant response
-- `preview: str` — flattened text preview
-
-### Spatial (`viewer_spatial`)
-- `id: str` — sample id
-- `task: str` — source task name (e.g. `refcoco`, `visual_genome`, `pointing`)
-- `image: Image` — first referenced image, embedded as JPEG bytes (downscaled to max edge 1024)
-- `image_paths: List[str]` — original relative image paths in the full dataset
-- `messages: List[{role, content}]` — chat-format Q/A
-- `preview: str` — flattened text preview
-
-## License
-
-Apache 2.0