acloudfan/embedded_movies_small

This dataset was created from the HuggingFace dataset AIatMongoDB/embedded_movies

Why was it needed?

1. The original dataset is close to 25 GB, for learning and experiments it is an overkill
2. Data in the dataset needs to be cleaned up e.g., some features are Null that requires extra care
3. 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

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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: 13791171 num_examples: 1021
• name: test num_bytes: 5811892 num_examples: 430 download_size: 19323013 dataset_size: 19603063 configs:
• config_name: default data_files:
  • split: train path: data/train-*
  • split: test path: data/test-* license: mit task_categories:
• text-classification
• question-answering
• zero-shot-classification
• sentence-similarity
• fill-mask
• text-to-speech language:
• en tags:
• movies
• embeddings
• sentiment analysis pretty_name: Movies data with plot-embeddings size_categories:
• 10M<n<100M

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