Clarify viewer table naming

Rename the Dataset Viewer Parquet file and split from train to viewer, and update the dataset card to clarify that the Parquet file is for visualization rather than a training split.

README.md

@@ -14,12 +14,12 @@ tags:
 - multimodal-reasoning
 - spatial-reasoning
 size_categories:
-- n<1K
+- 1K<n<10K
 configs:
 - config_name: default
   data_files:
-  - split: train
-    path: data/train-00000-of-00001.parquet
+  - split: viewer
+    path: data/viewer-00000-of-00001.parquet
 ---
 
 # EmbodiedNav-Bench
@@ -27,13 +27,13 @@ configs:
 [![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github&logoColor=white)](https://github.com/serenditipy-AC/Embodied-Navigation-Bench)
 [![arXiv](https://img.shields.io/badge/arXiv-2604.07973-b31b1b.svg?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2604.07973)
 
-EmbodiedNav-Bench is a goal-oriented embodied navigation benchmark for evaluating spatial action in urban 3D airspace. The dataset provides natural-language navigation goals, initial drone poses, target positions, and ground-truth 3D trajectories for embodied navigation evaluation.
+EmbodiedNav-Bench is a goal-oriented embodied navigation benchmark for evaluating spatial action in urban 3D airspace. The benchmark contains 5,037 high-quality navigation trajectories with natural-language navigation goals, initial drone poses, target positions, and ground-truth 3D trajectories.
 
 This Hugging Face repository hosts the dataset artifacts. The accompanying project code, simulator setup, media examples, and evaluation scripts are maintained in the GitHub repository: https://github.com/serenditipy-AC/Embodied-Navigation-Bench
 
 ## Dataset Summary
 
-The current release contains 300 navigation trajectories sampled from the EmbodiedNav-Bench benchmark. Each sample corresponds to one goal-oriented navigation task in an urban 3D environment, with a natural-language goal description and a human-collected ground-truth trajectory.
+The benchmark contains 5,037 goal-oriented navigation trajectories. Each sample corresponds to one navigation task in an urban 3D environment, with a natural-language goal description and a human-collected ground-truth trajectory.
 
 The dataset is intended for evaluating embodied navigation, spatial reasoning, and multimodal decision-making models in urban airspace scenarios.
 
@@ -43,7 +43,7 @@ The dataset is intended for evaluating embodied navigation, spatial reasoning, a
 | :-- | :-- |
 | `dataset/navi_data.pkl` | Canonical PKL file for evaluation. |
 | `dataset/navi_data_preview.json` | Human-readable JSON preview of the PKL content. |
-| `data/train-00000-of-00001.parquet` | Parquet conversion for the Hugging Face Dataset Viewer table. |
+| `data/viewer-00000-of-00001.parquet` | Parquet representation for the Hugging Face Dataset Viewer table. |
 
 ## Data Fields
 
@@ -60,7 +60,7 @@ The canonical PKL file stores a list of Python dictionaries. Each sample contain
 | `gt_traj` | `float[N,3]` | Ground-truth trajectory points. |
 | `gt_traj_len` | `float` | Ground-truth trajectory length. |
 
-The Parquet table includes the same structured fields and additional convenience columns such as `sample_index`, `start_x`, `start_y`, `start_z`, `target_x`, `target_y`, `target_z`, and `gt_traj_num_points`.
+The Parquet table includes the same structured fields and additional convenience columns such as `sample_index`, `start_x`, `start_y`, `start_z`, `target_x`, `target_y`, `target_z`, and `gt_traj_num_points`. It is provided for browsing and visualization in the Hugging Face Dataset Viewer and should not be interpreted as a training split.
 
 ## Usage
 
@@ -69,8 +69,8 @@ The Dataset Viewer-compatible table can be loaded with the `datasets` library:
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("EmbodiedCity/EmbodiedNav-Bench")
-print(ds["train"][0])
+ds = load_dataset("EmbodiedCity/EmbodiedNav-Bench", split="viewer")
+print(ds[0])
 ```
 
 For evaluation, use `dataset/navi_data.pkl` as the canonical data file and follow the setup instructions in the GitHub project repository.