Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: ValueError
Message: Feature type 'Int' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'Sequence', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 79, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
return HubDatasetModuleFactoryWithoutScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1237, in get_module
dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 464, in from_dataset_card_data
dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 395, in _from_yaml_dict
yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1918, in _from_yaml_list
return cls.from_dict(from_yaml_inner(yaml_data))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1758, in from_dict
obj = generate_from_dict(dic)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1400, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1400, in <dictcomp>
return {key: generate_from_dict(value) for key, value in obj.items()}
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1406, in generate_from_dict
raise ValueError(f"Feature type '{_type}' not found. Available feature types: {list(_FEATURE_TYPES.keys())}")
ValueError: Feature type 'Int' not found. Available feature types: ['Value', 'ClassLabel', 'Translation', 'TranslationVariableLanguages', 'Sequence', 'Array2D', 'Array3D', 'Array4D', 'Array5D', 'Audio', 'Image']Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata
Warning:
The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
No Robots Veriseti Kartı 🙅♂️🤖
Özet
No Robots 10000 komut ve gösterimden oluşan, profesyonel etiketleyiciler tarafından oluşturulmuş bir verisetidir. Çevirisi Google Cloud Platform Translation API ile yapıldı. Bu veriset LLM'lere komut takibi öğretmek için kullanılabilir. (Instruction Supervised Fine-tuning - SFT) No Robots veriseti OpenAI'ın InstructGPT makalesinden esinlenerek oluşturulmuştur ve aşağıdaki kategorilere sahiptir:
| Kategori | Adet |
|---|---|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
Diller
Bu verisetinde sadece Türkçe var.
Veriseti Yapısı
Bu verisetini CSV olarak yükledim. Örneklerin neye benzediğini görmek istiyorsanız widget'a bakın.
Veri Alanları
Kolonlar aşağıdaki gibidir:
prompt: Modelin takip etmesi gereken komutu belirler.prompt_id: Unique identifier.messages: Dictionary'ler içeren liste, her dictionary bir mesajı (key: content) ve o mesajı kimin gönderdiğini (key: role) açıklar.category: Görevin kategorisi, bunu çevirmedim.
Split'ler
| train_sft | test_sft | |
|---|---|---|
| no_robots | 9500 | 500 |
Lisans
Bu veriseti ne yazık ki açık kaynak değil açık erişimli. Lisansı Creative Commons NonCommercial (CC BY-NC 4.0). Eğer verisetinin kendisi açık kaynak olursa bu veriseti de açık kaynak olacaktır, çünkü çevirisini çeviriler üstünde fikri mülkiyet istemeyen GCP tarafından yaptım.
Citation
@misc{no_robots,
author = {Nazneen Rajani and Lewis Tunstall and Edward Beeching and Nathan Lambert and Alexander M. Rush and Thomas Wolf},
title = {No Robots},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/datasets/HuggingFaceH4/no_robots}}
}
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