metadata
language:
- en
license: mit
size_categories:
- 10M<n<100M
task_categories:
- text-classification
- table-question-answering
- fill-mask
- sentence-similarity
pretty_name: Movies Data with Embeddings
tags:
- movies
- embeddings
- sentiment
- vectors
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: rated
dtype: string
- name: writers
sequence: string
- name: runtime
dtype: float64
- name: num_mflix_comments
dtype: int64
- name: title
dtype: string
- name: cast
sequence: string
- name: plot
dtype: string
- name: directors
sequence: string
- name: type
dtype: string
- name: fullplot
dtype: string
- name: languages
sequence: string
- name: awards
struct:
- name: nominations
dtype: int64
- name: text
dtype: string
- name: wins
dtype: int64
- name: imdb
struct:
- name: id
dtype: int64
- name: rating
dtype: float64
- name: votes
dtype: int64
- name: plot_embedding
sequence: float64
- name: metacritic
dtype: float64
- name: countries
sequence: string
- name: genres
sequence: string
- name: poster
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 13712527
num_examples: 1015
- name: test
num_bytes: 5890390
num_examples: 436
download_size: 19307613
dataset_size: 19602917
This dataset was created from the HuggingFace dataset AIatMongoDB/embedded_movies
Why was it needed?
- The original dataset is close to 25 GB, for learning and experiments it is an overkill
- Data in the dataset needs to be cleaned up e.g., some features are Null that requires extra care
- Some of the embeddings are missing
How to use?
- Use for sentiment analysis
- Text similarity (plot)
- Embeddings : ready to use with vector DB & search libraries