Machine Learning QA Collection for Fine-Tuning, 12.4k Records

12.4k question-answer records were created by combining 7 source datasets for machine learning, data science, and AI. The dataset was used to test fine-tuning the Gemma 2 model by MLX on Apple Silicon. It was created by win-wang and last updated on 2024-09-25.

Use Cases

• Fine-tuning language models for machine learning question-answering based on the combined QA datasets.
• Benchmarking model performance on technical QA tasks based on the provided train/dev/test split.
• Creating educational chatbots or tutors for data science topics based on the extracted questions and answers.

Strengths

• Combines 7 source datasets for machine learning, data science, and AI.
• Contains approximately 12.4k records with a defined 7:1:2 train/dev/test split.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is an approximate figure; exact composition of the 7 source datasets is unknown.

Provenance

Source

huggingface

Collection Method

Combined and extracted questions and answers from 7 existing datasets.

Time Range

null

Freshness

Last updated 2024-09-25 04:27:25.

Geography

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