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---
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-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.
## 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
## 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 |