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

# NER_conllpp

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("wizardofchance/NER_conllpp")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 2
* Number of training documents: 26

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | peacekeeping - gandhi - terrorism - peace - terrorists | 19 | 0_peacekeeping_gandhi_terrorism_peace | 
| 1 | nations - organization - united - peace - council | 7 | 1_nations_organization_united_peace |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* 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
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.25.2
* HDBSCAN: 0.8.33
* UMAP: 0.5.6
* Pandas: 2.0.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.7.0
* Transformers: 4.40.1
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12