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README.md

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 ---
 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 some movies from the original dataset.
+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 bigger version [here](https://huggingface.co/datasets/mt0rm0/movie_descriptors).
+
+## Dataset Details
+
+### Dataset Description
+
+The dataset has 28655 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.
+
+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.
+ 
+**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 with 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)
+
+# filter movies according to:
+# - vote average of at least 6
+# - more than 100 votes
+# - revenue over 2M$
+df = df.loc[(df.vote_average >= 6)&(df.vote_count > 100)&(df.revenue > 2e6)]
+
+# 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?
+The source 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.