Datasets:

mt0rm0
/

movie_descriptors

	---
	license: cc0-1.0
	task_categories:
	- sentence-similarity
	language:
	- en
	pretty_name: '"Movie descriptors for Semantic Search"'
	size_categories:
	- 10K<n<100K
	tags:
	- movies
	- embeddings
	- semantic search
	- films
	- hpi
	- workshop
	---
	# Dataset Card

	This dataset is a subset from Kaggle's The Movie Dataset that contains only name, release year and overview for every film in the original dataset that has that information complete.
	It is intended as a toy dataset for learning about embeddings in a workshop from the AI Service Center Berlin-Brandenburg at the Hasso Plattner Institute.

	This dataset has a smaller version [here](https://huggingface.co/datasets/mt0rm0/movie_descriptors_small).

	## Dataset Details

	### Dataset Description

	The dataset has 44435 rows and 3 columns:

	- 'name': includes the title of the movies
	- 'release_year': indicates the year of release
	- 'overview': provides a brief description of each movie, used for advertisement.

	Curated by: [Mario Tormo Romero](https://huggingface.co/mt0rm0)

	Language(s) (NLP): English

	License: cc0-1.0

	### Dataset Sources

	This Dataset is a subset of Kaggle's [The Movie Dataset](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset).
	We have only used the <kbd>movies_metadata.csv</kbd> file and extracted some features (see Dataset Description) and dropped the rows that didn't were complete.

	The original Dataset has a cc0-1.0 License and we have maintained it.

	## Uses

	This is a toy dataset created for pegagogical purposes, and is used in the Working with embeddings Workshop created and organized by the [AI Service Center Berlin-Brandenburg](https://hpi.de/kisz/) at the [Hasso Plattner Institute](https://hpi.de/).

	## Dataset Creation

	### Curation Rationale

	We want to provide with this dataset a fast way of obtaining the required data for our workshops without having to download huge datasets that contain just way too much information.

	### Source Data

	Our source is Kaggle's The Movie Dataset., so the information comes from the MovieLens Dataset. The dataset consists of movies released on or before July 2017.

	#### Data Collection and Processing

	The data was downloaded from [Kaggle](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset) as a zip file. The file <kbd>movies_metadata.csv</kbd> was then extracted.

	The data was processed with the following code:

	```python
	import pandas as pd

	# load the csv file
	df = pd.read_csv("movies_metadata.csv", low_memory=False)

	# select the required columns, drop rows with missing values and
	# reset the index
	df = df.loc[:, ['title', 'release_date', 'overview']]
	df = df.dropna(axis=0).reset_index(drop=True)

	# make a new column with the release year
	df.loc[:, 'release_year'] = pd.to_datetime(df.release_date).dt.year

	# select the columns in the desired order
	df = df.loc[:, ['title', 'release_year', 'overview']]

	# save the data to parquet
	df.to_parquet('descriptors_data.parquet')
	```

	#### Who are the source data producers?

	This dataset is an ensemble of data collected by [Rounak Banik](https://www.kaggle.com/rounakbanik) from TMDB and GroupLens.
	In particular, the movies metadata has been collected from the TMDB Open API, but the source dataset is not endorsed or certified by TMDb.