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.

Usage

To use this model, please install BERTopic:

pip install -U bertopic safetensors

You can use the model as follows:

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

Click here for an overview of all topics.

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

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