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<urn:uuid:ab0a5c2f-e34c-4781-8bf8-6a3d1fd82649> | A local art historian and museum professional interested in 19th-century American art and the local cultural heritage of Cincinnati. | Alibaba-NLP/gte-large-en-v1.5 | [
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] | 1 | ["Education", "Academia", "Specialized Expertise"] | [
"Provide examples of 19th-century American art movements that significantly influenced the development of modern art in Cincinnati.",
"Detail the process of preserving historical American artworks considered valuable to the city's cultural heritage, including those owned by local museums.",
"Explain the significance of William Henry Brown's role in establishing the Agrippola County School for Young Colored Ladies, focusing on its influence on the education and arts of the African-American community in Cincinnati during the late 19th century.",
"What impact did the Great Cincinnati Art displaying of the Cincinnati Museum's acquisition of Antiques gave on the contemporary city residents' appreciation for the historical value of their city's art history"
] | {
"raw_input_evol_instruct_0": null,
"raw_input_self_instruct_0": [
{
"content": "# Task Description\nDevelop 5 user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model's textual capabilities.\n\n# Criteria for Queries\nIncorporate a diverse range of verbs, avoiding repetition.\nEnsure queries are compatible with AI model's text generation functions and are limited to 1-2 sentences.\nDesign queries to be self-contained and standalone.\nBlend interrogative (e.g., \"What is the significance of x?\") and imperative (e.g., \"Detail the process of x.\") styles.\nWrite each query on a separate line and avoid using numbered lists or bullet points.\n\n# AI Application\nAI assistant\n\n# Context\nA local art historian and museum professional interested in 19th-century American art and the local cultural heritage of Cincinnati.\n\n# Output\n",
"role": "user"
}
],
"raw_input_text_generation_0": null,
"raw_output_evol_instruct_0": null,
"raw_output_self_instruct_0": "Provide examples of 19th-century American art movements that significantly influenced the development of modern art in Cincinnati.\n\nDetail the process of preserving historical American artworks considered valuable to the city's cultural heritage, including those owned by local museums.\n\nExplain the significance of William Henry Brown's role in establishing the Agrippola County School for Young Colored Ladies, focusing on its influence on the education and arts of the African-American community in Cincinnati during the late 19th century.\n\nWhat impact did the Great Cincinnati Art displaying of the Cincinnati Museum's acquisition of Antiques gave on the contemporary city residents' appreciation for the historical value of their city's art history",
"raw_output_text_generation_0": null,
"statistics_self_instruct_0": {
"input_tokens": 205,
"output_tokens": 128
}
} | null | null | null |
Dataset Card for uplimit-synthetic-data-week-1-with-seed
This dataset has been created with distilabel.
The pipeline script was uploaded to easily reproduce the dataset: colab_kernel_launcher.py.
It can be run directly using the CLI:
distilabel pipeline run --script "https://huggingface.co/datasets/isabei/uplimit-synthetic-data-week-1-with-seed/raw/main/colab_kernel_launcher.py"
Dataset Summary
This dataset contains a pipeline.yaml
which can be used to reproduce the pipeline that generated it in distilabel using the distilabel
CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/isabei/uplimit-synthetic-data-week-1-with-seed/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config "https://huggingface.co/datasets/isabei/uplimit-synthetic-data-week-1-with-seed/raw/main/pipeline.yaml"
Dataset structure
The examples have the following structure per configuration:
Configuration: default
{
"cluster_label": 1,
"distilabel_metadata": {
"raw_input_evol_instruct_0": null,
"raw_input_self_instruct_0": [
{
"content": "# Task Description\nDevelop 5 user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model\u0027s textual capabilities.\n\n# Criteria for Queries\nIncorporate a diverse range of verbs, avoiding repetition.\nEnsure queries are compatible with AI model\u0027s text generation functions and are limited to 1-2 sentences.\nDesign queries to be self-contained and standalone.\nBlend interrogative (e.g., \"What is the significance of x?\") and imperative (e.g., \"Detail the process of x.\") styles.\nWrite each query on a separate line and avoid using numbered lists or bullet points.\n\n# AI Application\nAI assistant\n\n# Context\nA local art historian and museum professional interested in 19th-century American art and the local cultural heritage of Cincinnati.\n\n# Output\n",
"role": "user"
}
],
"raw_input_text_generation_0": null,
"raw_output_evol_instruct_0": null,
"raw_output_self_instruct_0": "Provide examples of 19th-century American art movements that significantly influenced the development of modern art in Cincinnati.\n\nDetail the process of preserving historical American artworks considered valuable to the city\u0027s cultural heritage, including those owned by local museums.\n\nExplain the significance of William Henry Brown\u0027s role in establishing the Agrippola County School for Young Colored Ladies, focusing on its influence on the education and arts of the African-American community in Cincinnati during the late 19th century.\n\nWhat impact did the Great Cincinnati Art displaying of the Cincinnati Museum\u0027s acquisition of Antiques gave on the contemporary city residents\u0027 appreciation for the historical value of their city\u0027s art history",
"raw_output_text_generation_0": null,
"statistics_self_instruct_0": {
"input_tokens": 205,
"output_tokens": 128
}
},
"embedding": [
0.022470641881227493,
-0.017901265993714333,
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0.06471165269613266,
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0.027933161705732346,
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"... (truncated - showing 10 of 1024 elements)"
],
"evolved_instruction": null,
"generation": null,
"id": "\u003curn:uuid:ab0a5c2f-e34c-4781-8bf8-6a3d1fd82649\u003e",
"instructions": [
"Provide examples of 19th-century American art movements that significantly influenced the development of modern art in Cincinnati.",
"Detail the process of preserving historical American artworks considered valuable to the city\u0027s cultural heritage, including those owned by local museums.",
"Explain the significance of William Henry Brown\u0027s role in establishing the Agrippola County School for Young Colored Ladies, focusing on its influence on the education and arts of the African-American community in Cincinnati during the late 19th century.",
"What impact did the Great Cincinnati Art displaying of the Cincinnati Museum\u0027s acquisition of Antiques gave on the contemporary city residents\u0027 appreciation for the historical value of their city\u0027s art history"
],
"model_name": null,
"model_name_embeddings": "Alibaba-NLP/gte-large-en-v1.5",
"nn_indices": [
15125,
37073,
71970,
26917,
4480,
69453,
71903,
9411,
39149,
59512,
"... (truncated - showing 10 of 20 elements)"
],
"nn_scores": [
0.8340727686882019,
0.8202860355377197,
0.8056823015213013,
0.8035473227500916,
0.7944107055664062,
0.7865492105484009,
0.7834657430648804,
0.7777583599090576,
0.7766708135604858,
0.7716180682182312,
"... (truncated - showing 10 of 20 elements)"
],
"persona": "A local art historian and museum professional interested in 19th-century American art and the local cultural heritage of Cincinnati.",
"projection": [
4.902285575866699,
1.8720051050186157
],
"summary_label": "[\"Education\", \"Academia\", \"Specialized Expertise\"]"
}
This subset can be loaded as:
from datasets import load_dataset
ds = load_dataset("isabei/uplimit-synthetic-data-week-1-with-seed", "default")
Or simply as it follows, since there's only one configuration and is named default
:
from datasets import load_dataset
ds = load_dataset("isabei/uplimit-synthetic-data-week-1-with-seed")
References
@misc{wang2023selfinstructaligninglanguagemodels,
title={Self-Instruct: Aligning Language Models with Self-Generated Instructions},
author={Yizhong Wang and Yeganeh Kordi and Swaroop Mishra and Alisa Liu and Noah A. Smith and Daniel Khashabi and Hannaneh Hajishirzi},
year={2023},
eprint={2212.10560},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2212.10560},
}
@misc{xu2023wizardlmempoweringlargelanguage,
title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
year={2023},
eprint={2304.12244},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2304.12244},
}
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