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


Hierarchy of topics:

![Hierarchy](https://i.imgur.com/Q8UHCQO.png)

## Usage 

To use this model, please install BERTopic:

```
pip install -U -q bertopic safetensors
```

You can use the model as follows:

```python
from bertopic import BERTopic
topic_model = BERTopic.load("pszemraj/BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-summary")

topic_model.visualize_topics()

# for dataframe:
# topic_model.get_topic_info()
```

predicting new instances:

```python
topic, embedding = topic_model.transform(text)
print(topic)
```


## Topic overview

* Number of topics: 24
* Number of training documents: 1960

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | no_saic_raw_sp - sep_4 - sec - data - image | 13 | -1_no_saic_raw_sp_sep_4_sec_data | 
| 0 | lecture - applications - methods - learning - topics | 104 | 0_lecture_applications_methods_learning | 
| 1 | cogvideo - videos - cogview2 - cog - video | 303 | 1_cogvideo_videos_cogview2_cog | 
| 2 | ship - rainsford - hunted - island - hunts | 117 | 2_ship_rainsford_hunted_island | 
| 3 | films - dissertation - film - noir - identity | 106 | 3_films_dissertation_film_noir | 
| 4 | linguistics - language - languages - foundational - systems | 104 | 4_linguistics_language_languages_foundational | 
| 5 | nemo - dory - transcript - clownfish - fish | 103 | 5_nemo_dory_transcript_clownfish | 
| 6 | train - bruno - washington - station - tennis | 102 | 6_train_bruno_washington_station | 
| 7 | images - representations - image - captions - representation | 102 | 7_images_representations_image_captions | 
| 8 | merge - merging - explain - concept - problems | 102 | 8_merge_merging_explain_concept | 
| 9 | enhancement - enhancing - recordings - improve - waveforms | 100 | 9_enhancement_enhancing_recordings_improve | 
| 10 | arendelle - elsa - frozen - kristoff - olaf | 99 | 10_arendelle_elsa_frozen_kristoff | 
| 11 | scene - story - script - movie - gillis | 97 | 11_scene_story_script_movie | 
| 12 | lecture - lemmatization - nlp - medical - techniques | 96 | 12_lecture_lemmatization_nlp_medical | 
| 13 | questions - topics - conversation - terrance - talk | 85 | 13_questions_topics_conversation_terrance | 
| 14 | sniper - kill - fury - combat - narrator | 81 | 14_sniper_kill_fury_combat | 
| 15 | images - lecture - ezurich - pathology - medical | 67 | 15_images_lecture_ezurich_pathology | 
| 16 | timeseries - framework - interpretability - representations - next_concept | 37 | 16_timeseries_framework_interpretability_representations | 
| 17 | prediction - predictions - forecasting - predict - markov | 27 | 17_prediction_predictions_forecasting_predict | 
| 18 | images - imaging - computational - convolutional - lecture | 27 | 18_images_imaging_computational_convolutional | 
| 19 | technology - treatment - methods - medical - detection | 27 | 19_technology_treatment_methods_medical | 
| 20 | novel - translation - henry - read - learn | 23 | 20_novel_translation_henry_read | 
| 21 | abridged - brief - synopsis - short - citations | 22 | 21_abridged_brief_synopsis_short | 
| 22 | lecture - pathology - medical - computational - patients | 16 | 22_lecture_pathology_medical_computational |
  
</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
{"language": ["en"], "license": "apache-2.0", "library_name": "bertopic", "tags": ["bertopic"], "datasets": ["pszemraj/summcomparer-gauntlet-v0p1"], "pipeline_tag": "text-classification", "inference": false}
bertopic text-classification en dataset:pszemraj/summcomparer-gauntlet-v0p1 license:apache-2.0 region:us
TAGS
#bertopic #text-classification #en #dataset-pszemraj/summcomparer-gauntlet-v0p1 #license-apache-2.0 #region-us 

BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-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.


Hierarchy of topics:


!Hierarchy


Usage
-----


To use this model, please install BERTopic:


You can use the model as follows:


predicting new instances:


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


* Number of topics: 24
* Number of training documents: 1960



Click here for an overview of all topics.


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