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--- |
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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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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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This dataset has a bigger version [here](https://huggingface.co/datasets/mt0rm0/movie_descriptors). |
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## Dataset Details |
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### Dataset Description |
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The dataset has 28655 rows and 3 columns: |
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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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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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**Curated by:** [Mario Tormo Romero](https://huggingface.co/mt0rm0) |
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**Language(s) (NLP):** English |
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**License:** cc0-1.0 |
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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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The original Dataset has a cc0-1.0 License and we have maintained it. |
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## Uses |
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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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## Dataset Creation |
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### Curation Rationale |
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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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### Source Data |
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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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#### Data Collection and Processing |
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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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# 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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# 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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# 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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# 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. |
