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---
language:
- en
license: cc-by-nc-sa-4.0
size_categories:
- 10B<n<100B
task_categories:
- other
pretty_name: ' Video2Reaction '
tags:
- video
- audio
- text
dataset_info:
features:
- name: video_id
dtype: string
- name: reaction_dominant
dtype: string
- name: num_key_frames
dtype: int32
- name: clip_description_embedding
sequence: float64
length: 768
- name: reaction_distribution
sequence: float64
length: 21
- name: movie_genre
sequence: float64
length: 23
- name: visual_feature
dtype:
array2_d:
shape:
- 176
- 768
dtype: float64
- name: audio_acoustic_feature
dtype:
array2_d:
shape:
- 176
- 1024
dtype: float64
- name: audio_semantic_feature
dtype:
array2_d:
shape:
- 176
- 1024
dtype: float64
splits:
- name: train
num_bytes: 28780644620
num_examples: 7243
- name: val
num_bytes: 4112655972
num_examples: 1035
- name: test
num_bytes: 8225311923
num_examples: 2070
download_size: 8946422642
dataset_size: 41118612515
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
# Video2Reaction
## Data Structure
* /data folder uploaded using `push_to_hub` python function. It is a duplicated version of the following content in hf autogenerated parquet file format
* Same data of the original file format are uploaded to the root directory
* `{split}_vit_bert-base-uncased_clap_general_hubert_large.pt` is a python dict that saves the torch tensor of the following latent features:
* visual_feature
* audio_acoustic_feature
* audio_semantic_feature
* clip_description_embedding
* The first three features' first dim is the time dim and it is not a fixed size dim, and it can range from 16 to 176. In the parquet version to fit into huggingface auto generating system (to get croissant meta) we pad every feature's time dim to the max length (176)
* `{split}.json` is the original meta file recording the video information
* Code repo: https://github.com/wm-bit/video2reaction
* REACTION_CLASSES
```python
REACTION_CLASSES = ['sadness', 'disgust', 'grief', 'fear', 'disapproval', 'disappointment', 'embarrassment', 'nervousness', 'annoyance', 'anger', 'confusion', 'realization', 'caring', 'curiosity', 'relief', 'approval', 'surprise', 'excitement', 'amusement', 'admiration', 'joy']
```
* MOVIW_GENRES
```python
MOVIW_GENRES = ['Music', 'Family', 'Crime', 'Thriller', 'Action', 'Western', 'Sci-Fi', 'Short', 'History', 'Adventure', 'Fantasy', 'Romance', 'Film-Noir', 'Biography', 'Comedy', 'Musical', 'War', 'Horror', 'Animation', 'Documentary', 'Sport', 'Mystery', 'Drama']
```
* SENTIMENT_2_FINER_GRAINED_MAPPING
```python
SENTIMENT_2_FINER_GRAINED_MAPPING = {
"positive": ["amusement", "excitement", "joy", "love", "desire", "optimism", "caring", "pride", "admiration", "gratitude", "relief", "approval"],
"negative": ["fear", "nervousness", "remorse", "embarrassment", "disappointment", "sadness", "grief", "disgust", "anger", "annoyance", "disapproval"],
"ambiguous": ["realization", "surprise", "curiosity", "confusion"]
}
```
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