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---
tags:
- bertopic
- summcomparer
library_name: bertopic
pipeline_tag: text-classification
inference: false
license: apache-2.0
datasets:
- pszemraj/summcomparer-gauntlet-v0p1
language:
- en
---
# BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-summary
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("pszemraj/BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-summary")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 25
* Number of training documents: 1960
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | question - it - going - they - she | 11 | -1_question_it_going_they |
| 0 | merging - merge - operations - concept - computation | 62 | 0_merging_merge_operations_concept |
| 1 | rainsford - island - sailors - hunted - hunting | 208 | 1_rainsford_island_sailors_hunted |
| 2 | film - films - noir - dissertation - cinema | 116 | 2_film_films_noir_dissertation |
| 3 | patients - predicting - predict - prediction - unsupervised | 114 | 3_patients_predicting_predict_prediction |
| 4 | cogvideo - videos - cogview2 - cog - pretrained | 108 | 4_cogvideo_videos_cogview2_cog |
| 5 | frozen - sled - snow - princess - hans | 108 | 5_frozen_sled_snow_princess |
| 6 | dory - coral - fish - gill - ocean | 103 | 6_dory_coral_fish_gill |
| 7 | captions - encoder - image - images - caption | 103 | 7_captions_encoder_image_images |
| 8 | law - assignments - lectures - assignment - learning | 99 | 8_law_assignments_lectures_assignment |
| 9 | convolutional - segmentation - imaging - pathology - superpixels | 98 | 9_convolutional_segmentation_imaging_pathology |
| 10 | enhancement - enhancing - vocoding - vocoder - audio | 97 | 10_enhancement_enhancing_vocoding_vocoder |
| 11 | tokenization - medical - health - words - embeddings | 97 | 11_tokenization_medical_health_words |
| 12 | gillis - scene - script - sunset - movie | 93 | 12_gillis_scene_script_sunset |
| 13 | anthony - antony - scene - guy - his | 92 | 13_anthony_antony_scene_guy |
| 14 | topic - projects - sociology - research - students | 90 | 14_topic_projects_sociology_research |
| 15 | peter - conversation - asks - questions - cheesy | 88 | 15_peter_conversation_asks_questions |
| 16 | sniper - marine - unarmed - combat - trained | 86 | 16_sniper_marine_unarmed_combat |
| 17 | communication - apparatus - method - input - embodiment | 68 | 17_communication_apparatus_method_input |
| 18 | words - phrases - political - unsupervised - topic | 27 | 18_words_phrases_political_unsupervised |
| 19 | clustering - similarity - unsupervised - topic - plagiarism | 23 | 19_clustering_similarity_unsupervised_topic |
| 20 | book - novel - father - read - arrives | 21 | 20_book_novel_father_read |
| 21 | topic - loans - clustering - loan - analyze | 19 | 21_topic_loans_clustering_loan |
| 22 | sciences - science - society - research - scientists | 16 | 22_sciences_science_society_research |
| 23 | dynamics - situation - quantum - mechanics - note | 13 | 23_dynamics_situation_quantum_mechanics |
</details>
### hierarchy
## 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: True
## Framework versions
* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11 |