---
tags:
- bertopic
- summcomparer
- document_text
library_name: bertopic
pipeline_tag: text-classification
inference: false
license: apache-2.0
datasets:
- pszemraj/summcomparer-gauntlet-v0p1
language:
- en
---

# BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-document_text

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. 

![docs-in-topics](https://i.imgur.com/5SzC0mt.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-sentence-t5-xl-document_text")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 16
* Number of training documents: 630

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | convolutional - images - networks - superpixels - overfitting | 12 | -1_convolutional_images_networks_superpixels | 
| 0 | bruno - guy - pdf - screentalk - he | 26 | 0_bruno_guy_pdf_screentalk | 
| 1 | elsa - arendelle - kristoff - frozen - anna | 94 | 1_elsa_arendelle_kristoff_frozen | 
| 2 | gillis - script - room - ll - artie | 73 | 2_gillis_script_room_ll | 
| 3 | interpretation - explanation - theory - structure - merge | 72 | 3_interpretation_explanation_theory_structure | 
| 4 | topics - topic - documents - corpus - document | 63 | 4_topics_topic_documents_corpus | 
| 5 | nemo - dory - chum - gill - fish | 56 | 5_nemo_dory_chum_gill | 
| 6 | films - film - identity - trauma - zinnemann | 54 | 6_films_film_identity_trauma | 
| 7 | computational - data - pathology - medical - informatics | 47 | 7_computational_data_pathology_medical | 
| 8 | images - captions - representations - embeddings - image | 26 | 8_images_captions_representations_embeddings | 
| 9 | zaroff - rainsford - hunt - hunting - general | 24 | 9_zaroff_rainsford_hunt_hunting | 
| 10 | cogvideo - interpolation - videos - coglm - frames | 24 | 10_cogvideo_interpolation_videos_coglm | 
| 11 | assignment - essays - questions - projects - students | 17 | 11_assignment_essays_questions_projects | 
| 12 | things - ll - some - lol - explain | 16 | 12_things_ll_some_lol | 
| 13 | videos - arxiv - visual - preprint - generative | 13 | 13_videos_arxiv_visual_preprint | 
| 14 | spectrograms - musecoder - melspectrogram - vocoding - spectrogram | 13 | 14_spectrograms_musecoder_melspectrogram_vocoding |
  
</details>

## 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