
# imdb_bertopic_ten_topics

This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. 
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. 

## Usage 

To use this model, please install BERTopic:

```
pip install -U bertopic
```

You can use the model as follows:

```python
from bertopic import BERTopic
topic_model = BERTopic.load("davanstrien/imdb_bertopic_ten_topics")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 10
* Number of training documents: 103062

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | film - movie - movies - character - characters | 44 | -1_film_movie_movies_character | 
| 0 | film - movie - films - movies - too | 25087 | 0_film_movie_films_movies | 
| 1 | episodes - shows - watching - tv - episode | 20955 | 1_episodes_shows_watching_tv | 
| 2 | films - film - movies - godzilla - movie | 2037 | 2_films_film_movies_godzilla | 
| 3 | cinderella - disney - cartoon - animation - cartoons | 895 | 3_cinderella_disney_cartoon_animation | 
| 4 | gameplay - games - game - adventure - starcraft | 465 | 4_gameplay_games_game_adventure | 
| 5 | holmes - sherlock - watson - doyle - conan | 228 | 5_holmes_sherlock_watson_doyle | 
| 6 | panther - film - films - clouseau - movies | 184 | 6_panther_film_films_clouseau | 
| 7 | metallica - metal - genres - genre - headbanger | 55 | 7_metallica_metal_genres_genre | 
| 8 | che - ernesto - castro - biopic - film | 50 | 8_che_ernesto_castro_biopic |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 40
* n_gram_range: (1, 1)
* nr_topics: 10
* seed_topic_list: None
* top_n_words: 10
* verbose: False

## Framework versions

* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11

{"library_name": "bertopic", "tags": ["bertopic"]}
bertopic region:us
TAGS
#bertopic #region-us 

imdb\_bertopic\_ten\_topics
===========================


This is a BERTopic model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.


Usage
-----


To use this model, please install BERTopic:


You can use the model as follows:


Topic overview
--------------


* Number of topics: 10
* Number of training documents: 103062



Click here for an overview of all topics.


Training hyperparameters
------------------------


* calculate\_probabilities: False
* language: None
* low\_memory: False
* min\_topic\_size: 40
* n\_gram\_range: (1, 1)
* nr\_topics: 10
* seed\_topic\_list: None
* top\_n\_words: 10
* verbose: False


Framework versions
------------------


* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11



TAGS
#bertopic #region-us 
