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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# xsum_108_3000_1500_test
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/xsum_108_3000_1500_test")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 3
* Number of training documents: 1500
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | yn - ar - ei - wedi - yr | 419 | -1_yn_ar_ei_wedi |
| 0 | said - mr - would - people - also | 9 | 0_said_mr_would_people |
| 1 | win - game - player - league - club | 1072 | 1_win_game_player_league |
</details>
## Training hyperparameters
* calculate_probabilities: True
* 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: 1.22.4
* 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: 5.13.1
* Python: 3.10.12
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