
# 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. 

![document-dist](https://i.imgur.com/rRRWBKL.png)
## 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

![hierarchy](https://i.imgur.com/BOgeWCa.png)

## 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
{"language": ["en"], "license": "apache-2.0", "library_name": "bertopic", "tags": ["bertopic", "summcomparer"], "datasets": ["pszemraj/summcomparer-gauntlet-v0p1"], "pipeline_tag": "text-classification", "inference": false}
bertopic summcomparer text-classification en dataset:pszemraj/summcomparer-gauntlet-v0p1 license:apache-2.0 region:us
TAGS
#bertopic #summcomparer #text-classification #en #dataset-pszemraj/summcomparer-gauntlet-v0p1 #license-apache-2.0 #region-us 

BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-summary
================================================================


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.


!document-dist


Usage
-----


To use this model, please install BERTopic:


You can use the model as follows:


Topic overview
--------------


* Number of topics: 25
* Number of training documents: 1960



Click here for an overview of all topics.


### hierarchy


!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



### hierarchy


!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
TAGS
#bertopic #summcomparer #text-classification #en #dataset-pszemraj/summcomparer-gauntlet-v0p1 #license-apache-2.0 #region-us 
 ### hierarchy


!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