MARTINI_enrich_BERTopic_CI_Lib

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("AIDA-UPM/MARTINI_enrich_BERTopic_CI_Lib")

topic_model.get_topic_info()

Topic overview

• Number of topics: 7
• Number of training documents: 419

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	1855 - 152 - 95 - lines - 139	22	-1_1855_152_95_lines
0	patrologiæ - volumes - preface - 161 - scanned	71	0_patrologiæ_volumes_preface_161
1	218 - 185 - 198 - 81 - 155	169	1_218_185_198_81
2	1855 - 165 - 186 - 206 - 56	62	2_1855_165_186_206
3	217 - 1855 - 157 - 197 - 57	37	3_217_1855_157_197
4	israelites - hamites - japhethites - miscegenation - simeon	30	4_israelites_hamites_japhethites_miscegenation
5	215 - 205 - 210 - 204 - 25	28	5_215_205_210_204

Training hyperparameters

• calculate_probabilities: True
• 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: 1.26.4
• HDBSCAN: 0.8.40
• UMAP: 0.5.7
• Pandas: 2.2.3
• Scikit-Learn: 1.5.2
• Sentence-transformers: 3.3.1
• Transformers: 4.46.3
• Numba: 0.60.0
• Plotly: 5.24.1
• Python: 3.10.12