# Text Clustering

Bge Large Medical
This is a model based on sentence-transformers that can map sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding
B
ls-da3m0ns
1,795
5
Hindi Sensim Sbert Usingsumodataset Basel3cubepune
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
H
gaurav-mac
27
0
QA Search
This is a model based on sentence-transformers that maps sentences and paragraphs into a 256-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.
Text Embedding Transformers
Q
omarelsayeed
29
0
Abc
This is a sentence similarity model based on sentence-transformers, which maps text to a 384-dimensional vector space for semantic search and clustering tasks.
Text Embedding Transformers
A
Nerdofdot
15
0
Finetuning Bm25 Small
This is a sentence similarity calculation model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space
Text Embedding
F
jhsmith
15
0
Finetunedsbert On 84 Million Triplets
This is a model based on sentence-transformers that can map sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
F
moslemsamiee
384
0
Turemb 512
This is a model based on sentence-transformers that maps sentences and paragraphs into a 512-dimensional dense vector space, suitable for tasks like clustering or semantic search.
Text Embedding Transformers
T
cenfis
16
3
Dfm Sentence Encoder Large Exp2 No Lang Align
This is a sentence encoder model based on sentence-transformers, capable of mapping sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as semantic search and clustering.
Text Embedding Transformers
D
KennethEnevoldsen
169
1
ALL Title Desc Curated
This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional vector space for sentence similarity computation and semantic search tasks.
Text Embedding Transformers
A
thtang
17
0
DISASTER MODEL PRECHATS
This is a model based on sentence-transformers that maps sentences and paragraphs into a 256-dimensional vector space for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
D
omarelsayeed
49
0
Roberta Topseg Contrastive
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
R
ighina
15
2
Stsb Bert Tiny Safetensors
This is a lightweight sentence embedding model based on the BERT architecture, capable of converting sentences and paragraphs into 128-dimensional dense vectors, suitable for tasks such as semantic similarity calculation.
Text Embedding Transformers
S
sentence-transformers-testing
136.99k
4
Sentence Transformers Multilingual E5 Large
MIT
This is a multilingual sentence embedding model based on sentence-transformers, capable of mapping text to a 1024-dimensional vector space, suitable for semantic search and clustering tasks.
Text Embedding
S
smart-tribune
276
0
Sentence Transformer Legal Hebert
This is a model based on sentence-transformers that can map sentences and paragraphs to a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
S
dean-ai
23
1
Sbert All MiniLM L6 V2
This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
S
nlplabtdtu
55
0
Constructionembeddingbert
This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 1536-dimensional dense vector space.
Text Embedding
C
ahhany
25
0
Dfm Sentence Encoder Small V1
This is a sentence encoder model based on sentence-transformers, capable of mapping sentences and paragraphs into a 256-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.
Text Embedding Transformers
D
kardosdrur
16
0
COS TAPT N RoBERTa Sts E3 OnlineContrastiveLoss 2023 10 16
This is a model based on sentence-transformers that maps sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.
Text Embedding Transformers
C
Kyleiwaniec
177
0
Indonesian Sbert Large
This is a sentence embedding model based on sentence-transformers, capable of converting text into 1024-dimensional vector representations, suitable for tasks such as semantic search and text similarity calculation.
Text Embedding Transformers
I
naufalihsan
92.89k
7
Finetuned Bge Embeddings
This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
F
austinpatrickm
17
0
Multi Qa Mpnet Base Dot V1 Covidqa Search Multiple Negatives Loss
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.
Text Embedding Transformers
M
checkiejan
14
0
M E5 Large Bs64 10 All Languages
This is a model based on sentence-transformers that maps sentences and paragraphs into a 1024-dimensional dense vector space for tasks such as sentence similarity calculation and semantic search.
Text Embedding
M
mrm8488
73
1
Finetuned Phobert Base V2
This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
F
owngpt
15
0
Multilingual E5 Large Skill Job Matcher
This is a sentence embedding model based on sentence-transformers, which can map text to a 1024-dimensional vector space and is suitable for semantic search and text similarity calculation.
Text Embedding
M
serbog
310
2
Frpile GPL Test Pipeline DragosGorduza FRPile MLM Basel 14000
This is a model based on sentence-transformers that maps sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
F
DragosGorduza
14
0
Frpile GPL Test Pipeline BAAI Bge Large En 14000
This is a model based on sentence-transformers that can map sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
F
DragosGorduza
14
0
Frpile GPL Test Pipeline All Mpnet Base V2 14000
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding
F
DragosGorduza
18
0
Msmarco Roberta Medxemoji V.1
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding Transformers
M
Norawit
19
0
Sentence T5 Large Quora Text Similarity
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering and semantic search.
Text Embedding
S
DrishtiSharma
103
2
S DagoBERT STSb
This is a sentence embedding model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, semantic search, and clustering.
Text Embedding Transformers
S
jpostma
13
0
Products Matching Aumet Fine Tune 2023 08 22
This is a model based on sentence-transformers that can map sentences and paragraphs to a 384-dimensional vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding
P
RIOLITE
21
0
Toolbench IR Bert Based Uncased
This is a sentence embedding model based on sentence-transformers, capable of converting text into 768-dimensional vector representations, suitable for tasks such as semantic search and text similarity calculation.
Text Embedding Transformers
T
ToolBench
342
19
Dfm Sentence Encoder Medium
This is a sentence encoder model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
D
KennethEnevoldsen
80
0
Sti Cyber Security Model Updated
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.
Text Embedding Transformers
S
BlueAvenir
116
0
Sbert Legal Xlm Roberta Base
This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for semantic similarity and feature extraction tasks.
Text Embedding Transformers
S
Stern5497
8,101
4
Mmarco Mnrl Bert Base Italian Uncased
This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
M
nickprock
153
1
Ai3 Bert Embedding Model
This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.
Text Embedding Transformers
A
jason1234
17
1
Mentioning Type Class Model
This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering and semantic search.
Text Embedding Transformers
M
BlueAvenir
13
0
Transformer
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation, clustering, and semantic search.
Text Embedding Transformers
T
kpourdeilami
44
0
Job Candidiate Matching Sentbert
This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding
J
duongttr
24
6
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