NER_conllpp
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:
pip install -U bertopic
You can use the model as follows:
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
Click here for an overview of all topics.
| 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 |
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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