Datasets:
Tasks:
Sentence Similarity
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
| 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. |