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  license: cc0-1.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: cc0-1.0
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+ task_categories:
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+ - sentence-similarity
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+ language:
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+ - en
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+ pretty_name: '"Movie descriptors for Semantic Search"'
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+ size_categories:
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+ - 10K<n<100K
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+ tags:
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+ - movies
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+ - embeddings
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+ - semantic search
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+ - films
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+ - hpi
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+ - workshop
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  ---
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+ # Dataset Card
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+
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+ This dataset is a subset from Kaggle's The Movie Dataset that contains only name, release year and overview for some movies from the original dataset.
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+ 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.
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+
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+ This dataset has a bigger version [here](https://huggingface.co/datasets/mt0rm0/movie_descriptors).
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+
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+ ## Dataset Details
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+
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+ ### Dataset Description
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+
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+ The dataset has 28655 rows and 3 columns:
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+
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+ - 'name': includes the title of the movies
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+ - 'release_year': indicates the year of release
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+ - 'overview': provides a brief description of each movie, used for advertisement.
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+
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+ The source dataset was filtered for keeping only movies with complete metadata in the required fields, a vote average of at least 6, with more than 100 votes and with a revenue over 2 Million Dollars.
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+
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+ **Curated by:** [Mario Tormo Romero](https://huggingface.co/mt0rm0)
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+
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+ **Language(s) (NLP):** English
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+
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+ **License:** cc0-1.0
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+
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+ ### Dataset Sources
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+ This Dataset is a subset of Kaggle's [The Movie Dataset](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset).
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+ 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.
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+
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+ The original Dataset has a cc0-1.0 License and we have maintained it.
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+
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+ ## Uses
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+
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+ 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/).
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ We want to provide with this dataset a fast way of obtaining the required data for our workshops without having to download huge datasets with just way too much information.
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+
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+ ### Source Data
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+
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+ 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.
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+
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+ #### Data Collection and Processing
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+
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+ 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.
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+ The data was processed with the following code:
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+ ```python
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+ import pandas as pd
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+ # load the csv file
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+ df = pd.read_csv("movies_metadata.csv", low_memory=False)
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+
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+ # filter movies according to:
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+ # - vote average of at least 6
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+ # - more than 100 votes
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+ # - revenue over 2M$
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+ df = df.loc[(df.vote_average >= 6)&(df.vote_count > 100)&(df.revenue > 2e6)]
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+
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+ # select the required columns, drop rows with missing values and
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+ # reset the index
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+ df = df.loc[:, ['title', 'release_date', 'overview']]
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+ df = df.dropna(axis=0).reset_index(drop=True)
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+
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+ # make a new column with the release year
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+ df.loc[:, 'release_year'] = pd.to_datetime(df.release_date).dt.year
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+ # select the columns in the desired order
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+ df = df.loc[:, ['title', 'release_year', 'overview']]
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+
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+ # save the data to parquet
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+ df.to_parquet('descriptors_data.parquet')
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+ ```
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+ #### Who are the source data producers?
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+ The source dataset is an ensemble of data collected by [Rounak Banik](https://www.kaggle.com/rounakbanik) from TMDB and GroupLens.
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+ In particular, the movies metadata has been collected from the TMDB Open API, but the source dataset is not endorsed or certified by TMDb.