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pszemraj/BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-summary

sourceHugging Faceapache-2.0updated 9mo agoView on Hugging Face

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BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-summary

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.

Hierarchy of topics:

Hierarchy

Usage

To use this model, please install BERTopic:

pip install -U -q bertopic safetensors

You can use the model as follows:

python

from bertopic import BERTopic
topic_model = BERTopic.load("pszemraj/BERTopic-summcomparer-gauntlet-v0p1-sentence-t5-xl-summary")

topic_model.visualize_topics()

# for dataframe:
# topic_model.get_topic_info()

predicting new instances:

python

topic, embedding = topic_model.transform(text)
print(topic)

Topic overview

—Number of topics: 24
—Number of training documents: 1960

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

Topic ID	Topic Keywords	Topic Frequency	Label
-1	nosaicrawsp - sep4 - sec - data - image	13	-1nosaicrawspsep4secdata
0	lecture - applications - methods - learning - topics	104	0lectureapplicationsmethodslearning
1	cogvideo - videos - cogview2 - cog - video	303	1cogvideovideoscogview2cog
2	ship - rainsford - hunted - island - hunts	117	2shiprainsfordhuntedisland
3	films - dissertation - film - noir - identity	106	3filmsdissertationfilmnoir
4	linguistics - language - languages - foundational - systems	104	4linguisticslanguagelanguagesfoundational
5	nemo - dory - transcript - clownfish - fish	103	5nemodorytranscriptclownfish
6	train - bruno - washington - station - tennis	102	6trainbrunowashingtonstation
7	images - representations - image - captions - representation	102	7imagesrepresentationsimagecaptions
8	merge - merging - explain - concept - problems	102	8mergemergingexplainconcept
9	enhancement - enhancing - recordings - improve - waveforms	100	9enhancementenhancingrecordingsimprove
10	arendelle - elsa - frozen - kristoff - olaf	99	10arendelleelsafrozenkristoff
11	scene - story - script - movie - gillis	97	11scenestoryscriptmovie
12	lecture - lemmatization - nlp - medical - techniques	96	12lecturelemmatizationnlpmedical
13	questions - topics - conversation - terrance - talk	85	13questionstopicsconversationterrance
14	sniper - kill - fury - combat - narrator	81	14sniperkillfurycombat
15	images - lecture - ezurich - pathology - medical	67	15imageslectureezurichpathology
16	timeseries - framework - interpretability - representations - next_concept	37	16timeseriesframeworkinterpretabilityrepresentations
17	prediction - predictions - forecasting - predict - markov	27	17predictionpredictionsforecastingpredict
18	images - imaging - computational - convolutional - lecture	27	18imagesimagingcomputationalconvolutional
19	technology - treatment - methods - medical - detection	27	19technologytreatmentmethodsmedical
20	novel - translation - henry - read - learn	23	20noveltranslationhenryread
21	abridged - brief - synopsis - short - citations	22	21abridgedbriefsynopsisshort
22	lecture - pathology - medical - computational - patients	16	22lecturepathologymedicalcomputational

</details>

Training hyperparameters

—calculate_probabilities: True
—language: None
—low_memory: False
—mintopicsize: 10
—ngramrange: (1, 1)
—nr_topics: None
—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: 4.29.2
—Numba: 0.56.4
—Plotly: 5.13.1
—Python: 3.10.11