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--- |
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license: mit |
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task_categories: |
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- text-generation |
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- question-answering |
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- summarization |
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language: |
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- en |
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tags: |
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- podcast |
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- transcription |
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- conversations |
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size_categories: |
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- n<1K |
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--- |
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# Lex Fridman Podcast Conversations Dataset |
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## Dataset Description |
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This dataset contains transcriptions of conversations from the Lex Fridman Podcast, featuring in-depth discussions on artificial intelligence, science, technology, philosophy, and more. The dataset includes 441 transcribed episodes, covering most of the podcast episodes up to January 2025 (excluding 10 episodes). |
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## Dataset Structure |
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### Features |
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- **Title**: String - The title of the podcast episode |
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- **Transcript**: String - The transcribed content of the episode |
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### Statistics |
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- Total number of examples: 441 |
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- Dataset size: 58.55 MB |
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- Download size: 31.56 MB |
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### Splits |
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- Training split: 441 examples |
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## Intended Uses |
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This dataset can be used for various natural language processing tasks including: |
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- Text Generation |
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- Summarization |
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- Question Answering |
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- Text-to-Speech Applications |
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## Technical Details |
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### Config Name |
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- lex-fridman-podcast-conversations |
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### Data Files |
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- Split: train |
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- Path: lex-fridman-podcast-conversations/train-* |
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### Language |
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- English |
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### Size Category |
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- n<1K (less than 1,000 examples) |
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## License |
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MIT License |
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## Citations & Attribution |
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Please ensure to cite: |
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1. The Lex Fridman Podcast as the original source of the conversations |
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2. This dataset if used in research or applications |
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## Notes |
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- The dataset excludes 10 episodes from the complete podcast series |
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- Last updated: January 20, 2025 |
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## Ethical Considerations |
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When using this dataset, please consider: |
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1. Proper attribution to the original content creator |
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2. Responsible use of the transcribed content |
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3. Respect for speaker privacy and context |
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4. Appropriate content filtering for downstream applications |
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## Technical Implementation |
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The dataset is structured for easy integration with common machine learning frameworks and can be loaded using standard dataset loading utilities. |
