---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# cnn_dailymail_6789_2000_1000_v1_train

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("KingKazma/cnn_dailymail_6789_2000_1000_v1_train")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 3
* Number of training documents: 2000

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | second - rider - minute - roma - teammate | 268 | -1_second_rider_minute_roma | 
| 0 | said - one - year - would - people | 1 | 0_said_one_year_would | 
| 1 | player - game - world - first - club | 1731 | 1_player_game_world_first |
  
</details>

## Training hyperparameters

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

## Framework versions

* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.31.0
* Numba: 0.57.1
* Plotly: 5.15.0
* Python: 3.10.12