Add BERTopic model
Browse files- README.md +72 -0
- config.json +16 -0
- ctfidf.safetensors +3 -0
- ctfidf_config.json +0 -0
- topic_embeddings.safetensors +3 -0
- topics.json +233 -0
README.md
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---
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tags:
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- bertopic
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library_name: bertopic
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pipeline_tag: text-classification
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---
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# MARTINI_enrich_BERTopic_zmoniuziniasklaida
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
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## Usage
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To use this model, please install BERTopic:
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```
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pip install -U bertopic
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```
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You can use the model as follows:
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```python
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from bertopic import BERTopic
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topic_model = BERTopic.load("AIDA-UPM/MARTINI_enrich_BERTopic_zmoniuziniasklaida")
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topic_model.get_topic_info()
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```
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## Topic overview
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* Number of topics: 3
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* Number of training documents: 131
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<details>
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<summary>Click here for an overview of all topics.</summary>
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| Topic ID | Topic Keywords | Topic Frequency | Label |
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|----------|----------------|-----------------|-------|
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| -1 | profilaktikos - vakcina - ukrainos - mobilizacijos - uzkreciamuju | 20 | -1_profilaktikos_vakcina_ukrainos_mobilizacijos |
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| 0 | vakcinacija - revakcinacijos - koronaviruso - testavimas - 152 | 82 | 0_vakcinacija_revakcinacijos_koronaviruso_testavimas |
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| 1 | lietuviu - statuto - demokratu - investicijas - preliminariasias | 29 | 1_lietuviu_statuto_demokratu_investicijas |
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</details>
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## Training hyperparameters
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* calculate_probabilities: True
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* language: None
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* low_memory: False
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* min_topic_size: 10
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* n_gram_range: (1, 1)
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* nr_topics: None
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* seed_topic_list: None
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* top_n_words: 10
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* verbose: False
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* zeroshot_min_similarity: 0.7
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* zeroshot_topic_list: None
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## Framework versions
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* Numpy: 1.26.4
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* HDBSCAN: 0.8.40
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* UMAP: 0.5.7
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* Pandas: 2.2.3
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* Scikit-Learn: 1.5.2
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* Sentence-transformers: 3.3.1
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* Transformers: 4.46.3
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* Numba: 0.60.0
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* Plotly: 5.24.1
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* Python: 3.10.12
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config.json
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{
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"calculate_probabilities": true,
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"language": null,
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"low_memory": false,
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"min_topic_size": 10,
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"n_gram_range": [
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1,
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1
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],
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"nr_topics": null,
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"seed_topic_list": null,
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"top_n_words": 10,
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"verbose": false,
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"zeroshot_min_similarity": 0.7,
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"zeroshot_topic_list": null
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}
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ctfidf.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:12e686037a4b2aefbc4d286ea801466b98fce87b4bb0a336d298bfc602b34175
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size 158396
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ctfidf_config.json
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See raw diff
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topic_embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4fc42e1d07861ea185b9885aac73e895ecf641f29a3cef7eb6096b9ab37db8c9
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size 12376
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topics.json
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{
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"topic_representations": {
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"-1": [
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[
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"profilaktikos",
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0.4668174386024475
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],
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[
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"vakcina",
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],
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[
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"ukrainos",
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],
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[
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"mobilizacijos",
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0.44405344128608704
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],
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[
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"uzkreciamuju",
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0.4278428554534912
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]
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],
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"0": [
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[
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"vakcinacija",
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],
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[
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"revakcinacijos",
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0.49907368421554565
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],
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[
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"koronaviruso",
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],
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[
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"testavimas",
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],
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[
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"152",
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]
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],
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"1": [
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[
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"lietuviu",
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],
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[
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"statuto",
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],
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[
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"demokratu",
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],
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[
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"investicijas",
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],
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[
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"preliminariasias",
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0.39936381578445435
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]
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]
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},
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"topics": [
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"topic_sizes": {
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"-1": 82,
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"1": 20,
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"0": 29
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},
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"topic_mapper": [
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[
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]
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],
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"topic_labels": {
|
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"-1": "-1_profilaktikos_vakcina_ukrainos_mobilizacijos",
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"0": "0_vakcinacija_revakcinacijos_koronaviruso_testavimas",
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"1": "1_lietuviu_statuto_demokratu_investicijas"
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},
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"custom_labels": null,
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"_outliers": 1,
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"topic_aspects": {}
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}
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