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tomvdwThe TensorFlow Datasets Authors
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Update the documentation
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docs/catalog/_toc.yaml

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- path: /datasets/catalog/dices
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title: dices
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- path: /datasets/catalog/wake_vision
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status: nightly
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title: wake_vision
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title: Age
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- section:
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title: Audio
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- section:
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- path: /datasets/catalog/ai2dcaption
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status: nightly
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title: ai2dcaption
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- path: /datasets/catalog/ogbg_molpcba
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title: ogbg_molpcba
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- path: /datasets/catalog/sift1m
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title: sift1m
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- path: /datasets/catalog/wake_vision
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status: nightly
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title: wake_vision
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title: Categorical
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- section:
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- path: /datasets/catalog/robomimic_ph
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title: robomimic_ph
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- path: /datasets/catalog/smart_buildings
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status: nightly
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title: smart_buildings
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title: Computer science
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- section:
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title: Document summarization
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- section:
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- path: /datasets/catalog/wake_vision
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status: nightly
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title: wake_vision
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title: Facial attributes
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- section:
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- path: /datasets/catalog/sun397
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title: sun397
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- path: /datasets/catalog/wake_vision
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status: nightly
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title: wake_vision
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title: Fine grained image classification
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- section:
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- path: /datasets/catalog/dices
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title: dices
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- path: /datasets/catalog/wake_vision
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status: nightly
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title: wake_vision
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title: Gender
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- section:
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title: Graphs
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- section:
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- path: /datasets/catalog/pneumonia_mnist
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status: nightly
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title: pneumonia_mnist
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title: Health
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- section:
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- path: /datasets/catalog/aflw2k3d
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title: aflw2k3d
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- path: /datasets/catalog/ai2dcaption
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status: nightly
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title: ai2dcaption
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- path: /datasets/catalog/bccd
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title: bccd
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- path: /datasets/catalog/plantae_k
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title: plantae_k
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- path: /datasets/catalog/pneumonia_mnist
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status: nightly
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title: pneumonia_mnist
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- path: /datasets/catalog/quickdraw_bitmap
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title: quickdraw_bitmap
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- path: /datasets/catalog/the300w_lp
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title: the300w_lp
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- path: /datasets/catalog/wake_vision
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status: nightly
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title: wake_vision
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title: Image
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- section:
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- path: /datasets/catalog/plant_village
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title: plant_village
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- path: /datasets/catalog/pneumonia_mnist
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status: nightly
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title: pneumonia_mnist
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- path: /datasets/catalog/resisc45
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title: resisc45 (manual)
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- path: /datasets/catalog/visual_domain_decathlon
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title: visual_domain_decathlon
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- path: /datasets/catalog/wake_vision
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status: nightly
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title: wake_vision
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title: Image classification
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- section:
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- path: /datasets/catalog/dices
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title: dices
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- path: /datasets/catalog/dolma
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status: nightly
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title: dolma
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- path: /datasets/catalog/e2e_cleaned
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title: e2e_cleaned
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- path: /datasets/catalog/dices
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title: dices
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- path: /datasets/catalog/dolma
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status: nightly
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title: dolma
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- path: /datasets/catalog/e2e_cleaned
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title: e2e_cleaned
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- path: /datasets/catalog/robomimic_ph
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title: robomimic_ph
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- path: /datasets/catalog/smart_buildings
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status: nightly
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title: smart_buildings
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title: Reinforcement learning
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- section:
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- section:
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- path: /datasets/catalog/aloha_mobile
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title: aloha_mobile
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- path: /datasets/catalog/asimov_dilemmas_auto_val
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title: asimov_dilemmas_auto_val
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- path: /datasets/catalog/asimov_dilemmas_scifi_train
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title: asimov_dilemmas_scifi_train
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- path: /datasets/catalog/asimov_dilemmas_scifi_val
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title: asimov_dilemmas_scifi_val
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- path: /datasets/catalog/asimov_injury_val
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title: asimov_injury_val
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- path: /datasets/catalog/asimov_multimodal_auto_val
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title: asimov_multimodal_auto_val
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- path: /datasets/catalog/asimov_multimodal_manual_val
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title: asimov_multimodal_manual_val
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- path: /datasets/catalog/asu_table_top_converted_externally_to_rlds
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title: asu_table_top_converted_externally_to_rlds
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- path: /datasets/catalog/austin_buds_dataset_converted_externally_to_rlds
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- path: /datasets/catalog/databricks_dolly
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title: databricks_dolly
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- path: /datasets/catalog/smart_buildings
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status: nightly
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title: smart_buildings
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title: Sequence modeling
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- section:
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- path: /datasets/catalog/doc_nli
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title: doc_nli
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- path: /datasets/catalog/dolma
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status: nightly
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title: dolma
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- path: /datasets/catalog/dolphin_number_word
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title: dolphin_number_word
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- path: /datasets/catalog/robomimic_ph
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title: robomimic_ph
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- path: /datasets/catalog/smart_buildings
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status: nightly
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title: smart_buildings
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- path: /datasets/catalog/smartwatch_gestures
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title: smartwatch_gestures

docs/catalog/ai2dcaption.md

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# `ai2dcaption`
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Note: This dataset was added recently and is only available in our
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`tfds-nightly` package
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<span class="material-icons" title="Available only in the tfds-nightly package">nights_stay</span>.
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* **Description**:
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This dataset is primarily based off the AI2D Dataset (see
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<div itemscope itemtype="http://schema.org/Dataset">
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<div itemscope itemprop="includedInDataCatalog" itemtype="http://schema.org/DataCatalog">
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<meta itemprop="name" content="TensorFlow Datasets" />
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</div>
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<meta itemprop="name" content="asimov_dilemmas_auto_val" />
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<meta itemprop="description" content="Binary dilemma questions generated from counterfactual situations used to auto-amend generated constitutions (validation set).&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;asimov_dilemmas_auto_val&#x27;, split=&#x27;train&#x27;)&#10;for ex in ds.take(4):&#10; print(ex)&#10;```&#10;&#10;See [the guide](https://www.tensorflow.org/datasets/overview) for more&#10;informations on [tensorflow_datasets](https://www.tensorflow.org/datasets).&#10;&#10;" />
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<meta itemprop="url" content="https://www.tensorflow.org/datasets/catalog/asimov_dilemmas_auto_val" />
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<meta itemprop="sameAs" content="https://asimov-benchmark.github.io/" />
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<meta itemprop="citation" content="@article{sermanet2025asimov,&#10; author = {Pierre Sermanet and Anirudha Majumdar and Alex Irpan and Dmitry Kalashnikov and Vikas Sindhwani},&#10; title = {Generating Robot Constitutions &amp; Benchmarks for Semantic Safety},&#10; journal = {arXiv preprint arXiv:2503.08663},&#10; url = {https://arxiv.org/abs/2503.08663},&#10; year = {2025},&#10;}" />
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</div>
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# `asimov_dilemmas_auto_val`
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* **Description**:
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Binary dilemma questions generated from counterfactual situations used to
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auto-amend generated constitutions (validation set).
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* **Homepage**:
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[https://asimov-benchmark.github.io/](https://asimov-benchmark.github.io/)
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* **Source code**:
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[`tfds.robotics.asimov.AsimovDilemmasAutoVal`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/robotics/asimov/asimov.py)
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* **Versions**:
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* **`0.1.0`** (default): Initial release.
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* **Download size**: `Unknown size`
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* **Dataset size**: `864.80 KiB`
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* **Auto-cached**
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([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
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Yes
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* **Splits**:
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Split | Examples
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:------ | -------:
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`'val'` | 34
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* **Feature structure**:
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```python
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FeaturesDict({
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'answer': Text(shape=(), dtype=string),
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'prompt_with_constitution': Text(shape=(), dtype=string),
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'prompt_with_constitution_antijailbreak': Text(shape=(), dtype=string),
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'prompt_with_constitution_antijailbreak_adversary': Text(shape=(), dtype=string),
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'prompt_with_constitution_antijailbreak_adversary_parts': Sequence(Text(shape=(), dtype=string)),
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'prompt_with_constitution_antijailbreak_parts': Sequence(Text(shape=(), dtype=string)),
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'prompt_with_constitution_parts': Sequence(Text(shape=(), dtype=string)),
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'prompt_without_constitution': Text(shape=(), dtype=string),
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'prompt_without_constitution_parts': Sequence(Text(shape=(), dtype=string)),
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})
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```
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* **Feature documentation**:
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Feature | Class | Shape | Dtype | Description
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:----------------------------------------------------- | :------------- | :------ | :----- | :----------
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| FeaturesDict | | |
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answer | Text | | string |
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prompt_with_constitution | Text | | string |
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prompt_with_constitution_antijailbreak | Text | | string |
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prompt_with_constitution_antijailbreak_adversary | Text | | string |
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prompt_with_constitution_antijailbreak_adversary_parts | Sequence(Text) | (None,) | string |
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prompt_with_constitution_antijailbreak_parts | Sequence(Text) | (None,) | string |
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prompt_with_constitution_parts | Sequence(Text) | (None,) | string |
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prompt_without_constitution | Text | | string |
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prompt_without_constitution_parts | Sequence(Text) | (None,) | string |
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* **Supervised keys** (See
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[`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
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`None`
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* **Figure**
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([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):
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Not supported.
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* **Examples**
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([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):
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<!-- mdformat off(HTML should not be auto-formatted) -->
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{% framebox %}
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<button id="displaydataframe">Display examples...</button>
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<div id="dataframecontent" style="overflow-x:auto"></div>
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<script>
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const url = "https://storage.googleapis.com/tfds-data/visualization/dataframe/asimov_dilemmas_auto_val-0.1.0.html";
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const dataButton = document.getElementById('displaydataframe');
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dataButton.addEventListener('click', async () => {
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// Disable the button after clicking (dataframe loaded only once).
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dataButton.disabled = true;
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const contentPane = document.getElementById('dataframecontent');
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try {
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const response = await fetch(url);
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// Error response codes don't throw an error, so force an error to show
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// the error message.
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if (!response.ok) throw Error(response.statusText);
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const data = await response.text();
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contentPane.innerHTML = data;
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} catch (e) {
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contentPane.innerHTML =
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'Error loading examples. If the error persist, please open '
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+ 'a new issue.';
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}
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});
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</script>
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{% endframebox %}
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<!-- mdformat on -->
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* **Citation**:
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```
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@article{sermanet2025asimov,
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author = {Pierre Sermanet and Anirudha Majumdar and Alex Irpan and Dmitry Kalashnikov and Vikas Sindhwani},
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title = {Generating Robot Constitutions & Benchmarks for Semantic Safety},
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journal = {arXiv preprint arXiv:2503.08663},
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url = {https://arxiv.org/abs/2503.08663},
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year = {2025},
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}
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```
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