metadata
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 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
You can use the model as follows:
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
Click here for an overview of all topics.
| 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 |
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