bertopic_sim70_10topics_larger_embed_raw

DobreMihai/bertopic_sim70_10topics_larger_embed_raw

bertopic_sim70_10topics_larger_embed_raw is a text classification model from DobreMihai. Use it when you need a label for a piece of text. It is set up for bertopic.

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.

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At a glance

Task: Text Classification
Library: bertopic
Access: Public
Created: Sep 21, 2024
Updated: Sep 21, 2024
SHA: bc0042b5

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Model card

From the Hugging Face model README

bertopic_sim70_10topics_larger_embed_raw

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("DobreMihai/bertopic_sim70_10topics_larger_embed_raw")

topic_model.get_topic_info()

Topic overview

Number of topics: 16
Number of training documents: 56774

<details> <summary>Click here for an overview of all topics.</summary>

Topic ID	Topic Keywords	Topic Frequency
0	loud - very - not - super - definitely	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	loud
1	subscription - be - but - joke - high	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	subscription
2	ad - bs - - -	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	ads
3	snooze - snoozing - never - what - no	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	snooze
4	premium - why - - -	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	premium*
5	math - need - the - more -	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	math
6	be - it - the - to - and	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	-1_be_it_the_to
7	app - be - the - it - to	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	0_app_be_the_it
8	good - nice - very - excellent - awesome	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	1_good_nice_very_excellent
9	work - easy - very - use - helpful	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	2_work_easy_very_use
10	hai - que - de - la - se	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	3_hai_que_de_la
11	ok - well - be - good - it	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	4_ok_well_be_good
12	super - epic - noice - top - excelent	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	5_super_epic_noice_top
13	life - annoying - change - save - it	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	6_life_annoying_change_save
14	never - reliable - fail - clock - dependable	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	7_never_reliable_fail_clock
15	step - math - squat - the - count	Topic Count
6 7 20334
15 6 17927
8 8 11505
7 9 3067
10 10 1362
9 11 1333
14 12 619
11 13 302
12 14 138
13 15 120
0 0 50
1 1 6
4 2 4
2 3 3
3 4 2
5 5 2	8_step_math_squat_the

</details>

Training hyperparameters

calculate_probabilities: False
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: False
zeroshot_min_similarity: 0.7
zeroshot_topic_list: None

Framework versions

Numpy: 1.26.4
HDBSCAN: 0.8.38.post1
UMAP: 0.5.6
Pandas: 2.2.1
Scikit-Learn: 1.5.2
Sentence-transformers: 3.1.0
Transformers: 4.44.2
Numba: 0.60.0
Plotly: 5.24.1
Python: 3.10.15

Task: Text Classification
Library: bertopic
Created: Sep 21, 2024
Updated: Sep 21, 2024