Add files using upload-large-folder tool

README.md

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+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- LeRobot
+- language_table
+- openx
+- google_robot
+configs:
+- config_name: default
+  data_files: data/*.parquet
+---
+Language Table (LeRobot) — Embedding-Only Release
+(DINOv3 + SigLIP2 image features; EmbeddingGemma task-text features)
+
+This repository packages a re-encoded variant of [IPEC-COMMUNITY/bc_z_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/bc_z_lerobot) where raw videos are replaced by fixed-length image embeddings, and task strings are augmented with text embeddings. All indices, splits, and semantics remain consistent with the source dataset while storage and I/O are substantially lighter. To make the dataset practical to upload/download and stream from the Hub, we also consolidated tiny per-episode Parquet files into N large Parquet shards under a single data/ folder. The file meta/sharded_index.json preserves a precise mapping from each original episode (referenced by a normalized identifier of the form data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet) to its shard path and row range, so you keep original addressing without paying the small-file tax.
+
+- Robot: google_robot
+- Modalities kept: states, actions, timestamps, frame/episode indices, image embeddings, task-text embeddings
+- Removed:
+- observation.images.image
+- License: apache-2.0 (inherits from source)
+
+----------------------------------------------------------------
+
+Quick Stats
+
+From meta/info.json and meta/task_text_embeddings_info.json:
+
+- Episodes: 39,350
+- Frames: 5,471,693
+- Tasks (unique): 104
+- Chunks (original layout): 40 (chunks_size=1000)
+- Shards (this release): 64 Parquet files under data/ (see meta/sharded_index.json)
+- FPS: 10
+- Image embeddings (per frame):
+  - observation.images.image_dinov3 → float32 [1024] (DINOv3 ViT-L/16 CLS)
+  - observation.images.image_siglip2 → float32 [768] (SigLIP2-base)
+- Task-text embeddings (per unique task):
+  - embedding → float32 [768] from google/embeddinggemma-300m
+  - Count: 104 rows (one per task)
+
+Note: This is an embedding-only package. The original pixel arrays listed under “Removed” are dropped.
+
+----------------------------------------------------------------
+
+<details>
+  <summary><b>Contents</b></summary>
+
+<pre>
+. 
+|-- meta/
+|   |-- info.json
+|   |-- sharded_index.json
+|   |-- tasks.jsonl
+|   |-- episodes.jsonl
+|   `-- task_text_embeddings_info.json
+|-- data/
+|   |-- shard-00000-of-000NN.parquet
+|   |-- shard-00001-of-000NN.parquet
+|   |-- ...
+|   `-- task_text_embeddings.parquet
+`-- README.md
+</pre>
+</details>
+
+----------------------------------------------------------------
+
+How This Was Generated (Reproducible Pipeline)
+
+1) Episode → Image Embeddings (drop pixels)
+convert_lerobot_to_embeddings_mono.py (GPU-accelerated preprocessing).
+Adds:
+- observation.images.image_dinov3 (float32[1024])
+- observation.images.image_siglip2 (float32[768])
+Removes:
+- observation.images.image
+
+2) Task-Text Embeddings (one row per unique task)
+build_task_text_embeddings.py with SentenceTransformer("google/embeddinggemma-300m") → data/task_text_embeddings.parquet + meta/task_text_embeddings_info.json.
+
+3) Data Consolidation (this release)
+All per-episode Parquets were consolidated into N large Parquet shards in one data/ folder.
+- The index meta/sharded_index.json records, for each episode, its normalized source identifier data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet, the destination shard path, and the (row_offset, num_rows) range inside that shard.
+- This preserves original addressing while making Hub sync/clone/stream far faster and more reliable.
+
+----------------------------------------------------------------
+
+Metadata (Excerpts)
+
+meta/task_text_embeddings_info.json
+
+~~~json
+{
+  "model": "google/embeddinggemma-300m",
+  "dimension": 768,
+  "normalized": true,
+  "count": 104,
+  "file": "task_text_embeddings.parquet"
+}
+~~~
+
+
+meta/info.json (embedding-only + shards)
+
+~~~json
+{
+  "codebase_version": "v2.0-embeddings-sharded",
+  "robot_type": "google_robot",
+  "total_episodes": 39350,
+  "total_frames": 5471693,
+  "total_tasks": 104,
+  "total_videos": 39350,
+  "total_chunks": 40,
+  "chunks_size": 1000,
+  "fps": 10,
+  "splits": {
+    "train": "0:39350"
+  },
+  "data_path": "data/shard-{shard_id:05d}-of-{num_shards:05d}.parquet",
+  "features": {
+    "observation.state": {
+      "dtype": "float32",
+      "shape": [
+        8
+      ],
+      "names": {
+        "motors": [
+          "x",
+          "y",
+          "z",
+          "roll",
+          "pitch",
+          "yaw",
+          "pad",
+          "gripper"
+        ]
+      }
+    },
+    "action": {
+      "dtype": "float32",
+      "shape": [
+        7
+      ],
+      "names": {
+        "motors": [
+          "x",
+          "y",
+          "z",
+          "roll",
+          "pitch",
+          "yaw",
+          "gripper"
+        ]
+      }
+    },
+    "timestamp": {
+      "dtype": "float32",
+      "shape": [
+        1
+      ],
+      "names": null
+    },
+    "frame_index": {
+      "dtype": "int64",
+      "shape": [
+        1
+      ],
+      "names": null
+    },
+    "episode_index": {
+      "dtype": "int64",
+      "shape": [
+        1
+      ],
+      "names": null
+    },
+    "index": {
+      "dtype": "int64",
+      "shape": [
+        1
+      ],
+      "names": null
+    },
+    "task_index": {
+      "dtype": "int64",
+      "shape": [
+        1
+      ],
+      "names": null
+    },
+    "observation.images.image_dinov3": {
+      "dtype": "float32",
+      "shape": [
+        1024
+      ],
+      "names": null
+    },
+    "observation.images.image_siglip2": {
+      "dtype": "float32",
+      "shape": [
+        768
+      ],
+      "names": null
+    }
+  },
+  "video_keys": [
+    "observation.images.image"
+  ],
+  "num_shards": 64,
+  "index_path": "meta/sharded_index.json"
+}
+~~~
+
+----------------------------------------------------------------
+
+Environment & Dependencies
+
+Python ≥ 3.9 • PyTorch ≥ 2.1 • transformers • sentence-transformers • pyarrow • tqdm • decord (and optionally av)
+
+----------------------------------------------------------------
+
+Provenance, License, and Citation
+
+- Source dataset: [IPEC-COMMUNITY/bc_z_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/bc_z_lerobot)
+- License: apache-2.0 (inherits from the source)
+- Encoders to cite:
+  - facebook/dinov3-vitl16-pretrain-lvd1689m
+  - google/siglip2-base-patch16-384
+  - google/embeddinggemma-300m
+
+----------------------------------------------------------------
+
+Changelog
+
+- v2.0-embeddings-sharded — Replaced video tensors with DINOv3 + SigLIP2 features; added EmbeddingGemma task-text embeddings; consolidated per-episode Parquets into N shards with a repo-local index; preserved original indexing/splits via normalized episode identifiers.

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meta/info.json

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+{
+  "codebase_version": "v2.0-embeddings-sharded",
+  "robot_type": "google_robot",
+  "total_episodes": 39350,
+  "total_frames": 5471693,
+  "total_tasks": 104,
+  "total_videos": 39350,
+  "total_chunks": 40,
+  "chunks_size": 1000,
+  "fps": 10,
+  "splits": {
+    "train": "0:39350"
+  },
+  "data_path": "data/shard-{shard_id:05d}-of-{num_shards:05d}.parquet",
+  "features": {
+    "observation.state": {
+      "dtype": "float32",
+      "shape": [
+        8
+      ],
+      "names": {
+        "motors": [
+          "x",
+          "y",
+          "z",
+          "roll",
+          "pitch",
+          "yaw",
+          "pad",
+          "gripper"
+        ]
+      }
+    },
+    "action": {
+      "dtype": "float32",
+      "shape": [
+        7
+      ],
+      "names": {
+        "motors": [
+          "x",
+          "y",
+          "z",
+          "roll",
+          "pitch",
+          "yaw",
+          "gripper"
+        ]
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+      "shape": [
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+    "observation.images.image_dinov3": {
+      "dtype": "float32",
+      "shape": [
+        1024
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+      "names": null
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+    "observation.images.image_siglip2": {
+      "dtype": "float32",
+      "shape": [
+        768
+      ],
+      "names": null
+    }
+  },
+  "video_keys": [
+    "observation.images.image"
+  ],
+  "num_shards": 64,
+  "index_path": "meta/sharded_index.json"
+}

meta/task_text_embeddings_info.json

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+{
+  "model": "google/embeddinggemma-300m",
+  "dimension": 768,
+  "normalized": true,
+  "count": 104,
+  "file": "task_text_embeddings.parquet"
+}

meta/tasks.jsonl

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+{"task_index": 0, "task": "place the white sponge in the ceramic bowl"}
+{"task_index": 1, "task": "move the arm in a circular motion"}
+{"task_index": 2, "task": "place the bottle in the ceramic bowl"}
+{"task_index": 3, "task": "drag the ceramic bowl in a circle"}
+{"task_index": 4, "task": "place apple in tray"}
+{"task_index": 5, "task": "place eraser in metal cup"}
+{"task_index": 6, "task": "place sponge in tray"}
+{"task_index": 7, "task": "place the eraser on the white sponge"}
+{"task_index": 8, "task": "wipe tray with sponge"}
+{"task_index": 9, "task": "pick up the ceramic bowl"}
+{"task_index": 10, "task": "stack bowls into tray"}
+{"task_index": 11, "task": "place the pepper on the white sponge"}
+{"task_index": 12, "task": "place banana in red bowl"}
+{"task_index": 13, "task": "stack cups into tray"}
+{"task_index": 14, "task": "place the ceramic cup in the ceramic bowl"}
+{"task_index": 15, "task": "wipe table surface with eraser"}
+{"task_index": 16, "task": "place fork in purple bowl"}
+{"task_index": 17, "task": "place the pepper in the ceramic bowl"}
+{"task_index": 18, "task": "place the white sponge in the ceramic cup"}
+{"task_index": 19, "task": "knock the bottle over"}
+{"task_index": 20, "task": "place fork in plastic cup"}
+{"task_index": 21, "task": "place banana in tray"}
+{"task_index": 22, "task": "wipe table surface with brush"}
+{"task_index": 23, "task": "place grapes in table surface"}
+{"task_index": 24, "task": "push the eraser across the table"}
+{"task_index": 25, "task": "place brush in plastic cup"}
+{"task_index": 26, "task": "place the eraser in the ceramic cup"}
+{"task_index": 27, "task": "place the pepper in the ceramic cup"}
+{"task_index": 28, "task": "place the ceramic cup over the eraser"}
+{"task_index": 29, "task": "knock the eraser over"}
+{"task_index": 30, "task": "place apple in plastic cup"}
+{"task_index": 31, "task": "place sponge in table surface"}
+{"task_index": 32, "task": "push the ceramic bowl across the table"}
+{"task_index": 33, "task": "pick up the ceramic cup"}
+{"task_index": 34, "task": "wipe tray with brush"}
+{"task_index": 35, "task": "place fork in metal cup"}
+{"task_index": 36, "task": "place apple in red bowl"}
+{"task_index": 37, "task": "wipe purple bowl with eraser"}
+{"task_index": 38, "task": "place eraser in plastic cup"}
+{"task_index": 39, "task": "wipe red bowl with brush"}
+{"task_index": 40, "task": "place pepper in plastic cup"}
+{"task_index": 41, "task": "place eraser in table surface"}
+{"task_index": 42, "task": "place eraser in tray"}
+{"task_index": 43, "task": "drag the pepper across the table"}
+{"task_index": 44, "task": "place brush in metal cup"}
+{"task_index": 45, "task": "stack bowls on top of each other"}
+{"task_index": 46, "task": "wipe purple bowl with sponge"}
+{"task_index": 47, "task": "place apple in paper cup"}
+{"task_index": 48, "task": "wipe the white sponge on the table"}
+{"task_index": 49, "task": "place banana in table surface"}
+{"task_index": 50, "task": "place pepper in metal cup"}
+{"task_index": 51, "task": "place grapes in red bowl"}
+{"task_index": 52, "task": "wipe table surface with sponge"}
+{"task_index": 53, "task": "place pepper in purple bowl"}
+{"task_index": 54, "task": "place eraser in purple bowl"}
+{"task_index": 55, "task": "wipe red bowl with sponge"}
+{"task_index": 56, "task": "place brush in table surface"}
+{"task_index": 57, "task": "place fork in table surface"}
+{"task_index": 58, "task": "place eraser in paper cup"}
+{"task_index": 59, "task": "stand the bottle upright"}
+{"task_index": 60, "task": "place brush in paper cup"}
+{"task_index": 61, "task": "place sponge in purple bowl"}
+{"task_index": 62, "task": "place towel in red bowl"}
+{"task_index": 63, "task": "place pepper in red bowl"}
+{"task_index": 64, "task": "place apple in metal cup"}
+{"task_index": 65, "task": "place eraser in red bowl"}
+{"task_index": 66, "task": "place grapes in tray"}
+{"task_index": 67, "task": "wipe red bowl with eraser"}
+{"task_index": 68, "task": "place towel in paper cup"}
+{"task_index": 69, "task": "place brush in tray"}
+{"task_index": 70, "task": "place sponge in plastic cup"}
+{"task_index": 71, "task": "place fork in tray"}
+{"task_index": 72, "task": "place pepper in paper cup"}
+{"task_index": 73, "task": "place sponge in red bowl"}
+{"task_index": 74, "task": "wipe red bowl with towel"}
+{"task_index": 75, "task": "place apple in purple bowl"}
+{"task_index": 76, "task": "wipe purple bowl with brush"}
+{"task_index": 77, "task": "place brush in purple bowl"}
+{"task_index": 78, "task": "place pepper in table surface"}
+{"task_index": 79, "task": "place grapes in paper cup"}
+{"task_index": 80, "task": "place grapes in purple bowl"}
+{"task_index": 81, "task": "wipe purple bowl with towel"}
+{"task_index": 82, "task": "place grapes in metal cup"}
+{"task_index": 83, "task": "place fork in red bowl"}
+{"task_index": 84, "task": "place towel in metal cup"}
+{"task_index": 85, "task": "place pepper in tray"}
+{"task_index": 86, "task": "place towel in table surface"}
+{"task_index": 87, "task": "place sponge in metal cup"}
+{"task_index": 88, "task": "place towel in plastic cup"}
+{"task_index": 89, "task": "place banana in plastic cup"}
+{"task_index": 90, "task": "wipe tray with eraser"}
+{"task_index": 91, "task": "stack cups on top of each other"}
+{"task_index": 92, "task": "place fork in paper cup"}
+{"task_index": 93, "task": "wipe table surface with towel"}
+{"task_index": 94, "task": "place banana in metal cup"}
+{"task_index": 95, "task": "place towel in purple bowl"}
+{"task_index": 96, "task": "place banana in purple bowl"}
+{"task_index": 97, "task": "wipe tray with towel"}
+{"task_index": 98, "task": "place towel in tray"}
+{"task_index": 99, "task": "place grapes in plastic cup"}
+{"task_index": 100, "task": "place sponge in paper cup"}
+{"task_index": 101, "task": "place banana in paper cup"}
+{"task_index": 102, "task": "place brush in red bowl"}
+{"task_index": 103, "task": "place apple in table surface"}