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test_hf_data

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- ---
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- license: apache-2.0
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- viewer: true
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- ---
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-
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- # ZebangCheng/test_hf_data
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-
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- ## Dataset Summary
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- A collection of short video clips annotated with emotions, stored efficiently using Parquet format with embedded video paths.
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-
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- ## Dataset Fields
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-
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- | Field | Type | Description |
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- |------|------|-------------|
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- | video_path | string | Path to the video file (MP4 format) |
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- | label | string | Emotion label, e.g., happy, sad, angry, neutral, etc. |
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- | description | string | Short description of the video content |
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- | video_id | string | Unique video identifier |
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-
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- ## Example
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-
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- | video_path | label | description | video_id |
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- |------------|------|-------------|----------|
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- | video/00019.mp4 | neutral | It's me. | 00019.mp4 |
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-
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- ## How to Use
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-
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- ```python
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- from datasets import load_dataset
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-
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- dataset = load_dataset("ZebangCheng/test_hf_data", split='train')
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- print(dataset[0]['video_path']) # Path to video
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- print(dataset[0]['label']) # Emotion label
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- print(dataset[0]['description']) # Video description
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- print(dataset[0]['video_id']) # Video ID
 
 
 
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+ ---
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+ license: artistic-2.0
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+ viewer: true
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+ size_categories:
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+ - n<1K
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+ ---
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+
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+ # ZebangCheng/test_hf_data
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+
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+ ## Dataset Summary
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+ A collection of short video clips annotated with emotions, stored efficiently using Parquet format with embedded video paths.
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+
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+ ## Dataset Fields
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+
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+ | Field | Type | Description |
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+ |------|------|-------------|
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+ | video_path | string | Path to the video file (MP4 format) |
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+ | label | string | Emotion label, e.g., happy, sad, angry, neutral, etc. |
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+ | description | string | Short description of the video content |
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+ | video_id | string | Unique video identifier |
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+
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+ ## Example
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+
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+ | video_path | label | description | video_id |
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+ |------------|------|-------------|----------|
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+ | video/00019.mp4 | neutral | It's me. | 00019.mp4 |
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+
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+ ## How to Use
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("ZebangCheng/test_hf_data", split='train')
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+ print(dataset[0]['video_path']) # Path to video
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+ print(dataset[0]['label']) # Emotion label
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+ print(dataset[0]['description']) # Video description
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+ print(dataset[0]['video_id']) # Video ID