Modelpublic

Kamaljp/topic_docs5000

sourceHugging Faceupdated 3y agoView on Hugging Face

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tags:

—bertopic libraryname: bertopic pipelinetag: text-classification ---

topic_docs5000

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:

python

from bertopic import BERTopic
topic_model = BERTopic.load("Kamaljp/topic_docs5000")

topic_model.get_topic_info()

Topic overview

—Number of topics: 30
—Number of training documents: 5000

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

Topic ID	Topic Keywords	Topic Frequency	Label
-1	the - to - of - and - is	12	-1thetoofand
0	the - in - to - he - game	1606	0theintohe
1	the - drive - to - with - for	450	1thedrivetowith
2	the - to - that - of - and	344	2thetothatof
3	the - of - and - in - to	246	3theofandin
4	of - to - the - is - and	220	4oftotheis
5	the - car - and - it - for	203	5thecarandit
6	the - of - that - to - is	186	6theofthatto
7	call - three - bittrolff - uhhhh - test	172	7callthreebittrolffuhhhh
8	the - to - be - of - key	172	8thetobeof
9	the - space - of - and - to	169	9thespaceofand
10	the - openwindows - to - window - and	169	10theopenwindowstowindow
11	for - and - 100 - to - the	146	11forand100to
12	windows - dos - the - and - to	132	12windowsdostheand
13	the - bike - to - my - was	105	13thebiketomy
14	you - that - to - of - your	100	14youthattoof
15	for - and - to - mail - send	100	15forandtomail
16	to - that - homosexual - of - is	94	16tothathomosexualof
17	is - that - objective - of - science	66	17isthatobjectiveof
18	printer - fonts - deskjet - hp - the	56	18printerfontsdeskjethp
19	jpeg - image - gif - file - format	45	19jpegimagegiffile
20	points - graeme - polygon - the - lines	44	20pointsgraemepolygonthe
21	radar - detector - detectors - is - the	28	21radardetectordetectorsis
22	hotel - dj - for - ticket - price	27	22hoteldjforticket
23	insurance - health - private - the - and	26	23insurancehealthprivatethe
24	water - battery - temperature - the - discharge	21	24waterbatterytemperaturethe
25	oil - paint - it - wax - and	17	25oilpaintitwax
26	drugs - cocaine - lsd - drug - license	16	26drugscocainelsddrug
27	motif - toolkit - cosecomplient - api - mean	15	27motiftoolkitcosecomplientapi
28	maxaxaxaxaxaxaxaxaxaxaxaxaxaxax - entry - entries - rules - we	13	28maxaxaxaxaxaxaxaxaxaxaxaxaxaxaxentryentriesrules

</details>

Training hyperparameters

—calculate_probabilities: True
—language: english
—low_memory: False
—mintopicsize: 10
—ngramrange: (1, 1)
—nr_topics: 30
—seedtopiclist: None
—topnwords: 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: 4.30.2
—Numba: 0.56.4
—Plotly: 5.13.1
—Python: 3.10.12