README.md

---
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?**

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


---
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
---