
# BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-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. 

![doc-chunk-topics](document-chunk-topics.png)

## Usage 

To use this model, please install BERTopic:

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

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-document_text")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 17
* Number of training documents: 995

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | clustering - convolutional - neural - hierarchical - autoregressive | 11 | -1_clustering_convolutional_neural_hierarchical | 
| 0 | betty - door - her - gillis - room | 15 | 0_betty_door_her_gillis | 
| 1 | frozen - anna - snow - hans - elsa | 241 | 1_frozen_anna_snow_hans | 
| 2 | closeup - shot - viewpoint - umpire - camera | 211 | 2_closeup_shot_viewpoint_umpire | 
| 3 | dory - gill - coral - marlin - ocean | 171 | 3_dory_gill_coral_marlin | 
| 4 | operations - structure - operation - theory - interpretation | 60 | 4_operations_structure_operation_theory | 
| 5 | spatial - identity - movement - identities - noir | 59 | 5_spatial_identity_movement_identities | 
| 6 | vocabulary - words - topic - text - topics | 45 | 6_vocabulary_words_topic_text | 
| 7 | encoder - captions - embeddings - decoder - caption | 40 | 7_encoder_captions_embeddings_decoder | 
| 8 | saw - hounds - smiled - had - hunt | 26 | 8_saw_hounds_smiled_had | 
| 9 | learning - assignment - data - research - project | 22 | 9_learning_assignment_data_research | 
| 10 | cogvideo - videos - videogpt - video - clips | 21 | 10_cogvideo_videos_videogpt_video | 
| 11 | lstm - recurrent - encoder - seq2seq - neural | 18 | 11_lstm_recurrent_encoder_seq2seq | 
| 12 | improve - next - do - going - good | 17 | 12_improve_next_do_going | 
| 13 | vocoding - spectrogram - enhancement - melspectrogram - audio | 14 | 13_vocoding_spectrogram_enhancement_melspectrogram | 
| 14 | probabilities - tagging - probability - words - gram | 12 | 14_probabilities_tagging_probability_words | 
| 15 | convolutional - segmentation - superpixel - convolutions - superpixels | 12 | 15_convolutional_segmentation_superpixel_convolutions |
  
</details>

### hierarchy

![h](https://i.imgur.com/TLa6jXT.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", "document_text"], "datasets": ["pszemraj/summcomparer-gauntlet-v0p1"], "pipeline_tag": "text-classification", "inference": false}
bertopic summcomparer document_text text-classification en dataset:pszemraj/summcomparer-gauntlet-v0p1 license:apache-2.0 region:us
TAGS
#bertopic #summcomparer #document_text #text-classification #en #dataset-pszemraj/summcomparer-gauntlet-v0p1 #license-apache-2.0 #region-us 

BERTopic-summcomparer-gauntlet-v0p1-all-roberta-large-v1-document\_text
=======================================================================


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.


!doc-chunk-topics


Usage
-----


To use this model, please install BERTopic:


You can use the model as follows:


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


* Number of topics: 17
* Number of training documents: 995



Click here for an overview of all topics.


### hierarchy


!h


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


!h


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


!h


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