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---
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
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