# Semantic Similarity
Qwen3 Embedding 0.6B W4A16 G128
Apache-2.0
GPTQ quantized version of Qwen3-Embedding-0.6B, optimized for video memory usage with minimal performance loss
Text Embedding
Q
boboliu
131
2
Ruri V3 Pt 30m
Apache-2.0
Ruri is a Japanese universal text embedding model based on ModernBERT-Ja, offering versions with different parameter scales suitable for various text processing tasks.
Text Embedding
Safetensors Japanese
R
cl-nagoya
250
1
Fingumv3
This is a sentence-transformers model fine-tuned from dunzhang/stella_en_1.5B_v5, designed to generate 1024-dimensional dense vector representations for sentences and paragraphs, suitable for tasks like semantic text similarity and semantic search.
Text Embedding
F
FINGU-AI
26
1
Noinstruct Small Embedding V0
MIT
NoInstruct Small Embedding Model v0 is an improved embedding model focused on enhancing retrieval task performance while maintaining independence from arbitrary instruction encoding.
Text Embedding
Transformers English

N
avsolatorio
90.76k
22
Paraphrase MiniLM L6 V2 Finetune Summary
A sentence embedding model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and text similarity calculation
Text Embedding
Transformers

P
tonychenxyz
20
1
Gte Large Gguf
MIT
GGUF format version of the General Text Embedding (GTE) model, suitable for tasks like information retrieval and semantic text similarity.
Text Embedding English
G
ChristianAzinn
184
1
K Finance Sentence Transformer
This is a sentence-transformers-based sentence embedding model that maps text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.
Text Embedding
Transformers

K
ohsuz
160
1
Gte Base Onnx
Apache-2.0
GTE-Base is a general-purpose text embedding model capable of converting text into high-dimensional vector representations, suitable for text classification and similarity search tasks.
Text Embedding
Transformers

G
Qdrant
31
3
Gte Large Onnx
Apache-2.0
GTE-Large is a text embedding model ported to ONNX, suitable for text classification and similarity search tasks.
Text Embedding
Transformers

G
Qdrant
597
2
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
Supervised Ft Embedding 1203 V1
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
S
li-ping
19
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
Multilingual E5 Small Optimized
MIT
This is the quantized version of multilingual-e5-small, optimized for inference performance through layer-wise quantization while retaining most of the original model's quality.
Text Embedding Supports Multiple Languages
M
elastic
201
15
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
Sbert All MiniLM L6 V2
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
Transformers

S
patent
34
2
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
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
E5 Large En Ru
MIT
This is a vocabulary-pruned version of the intfloat/multilingual-e5-large model, retaining only Russian and English tokens while maintaining the original model's performance.
Text Embedding
Transformers Supports Multiple Languages

E
d0rj
712
9
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
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
Products Matching Aumet
This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding
P
RIOLITE
19
1
Arabic KW Mdel
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, clustering, and semantic search.
Text Embedding
Transformers

A
medmediani
15.84k
5
Keysentence Finder
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 similarity calculation.
Text Embedding
Transformers

K
m3hrdadfi
31
0
Evaluation Xlm Roberta Model
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

E
loutchy
22
0
Relevance Classifier Model
This is a sentence embedding model based on sentence-transformers, capable of converting text into 384-dimensional vector representations.
Text Embedding
Transformers

R
tatianafp
175
0
Dragon Plus Context Encoder
DRAGON+ is a dense retrieval model based on the BERT architecture, employing an asymmetric dual-encoder architecture, suitable for text retrieval tasks.
Text Embedding
Transformers

D
facebook
4,396
39
Trecdl22 Crossencoder Debertav3
A Transformer-based text ranking model for sorting sentences or paragraphs by relevance.
Text Embedding
T
naver
9,226
1
Nooks Amd Detection Realtime
This is a model based on sentence-transformers that maps sentences and paragraphs into a 768-dimensional dense vector space for tasks such as sentence similarity calculation and semantic search.
Text Embedding
Transformers

N
nikcheerla
17
0
SBERT JSNLI Base
This is a model based on sentence-transformers, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space for tasks such as sentence similarity calculation, clustering, and semantic search.
Text Embedding
Transformers

S
MU-Kindai
343
0
579 STmodel Product Rem V3a
This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.
Text Embedding
Transformers

5
jamiehudson
15
0
Address Match Abp V2
This is a model based on sentence-transformers that maps sentences and paragraphs into a 64-dimensional dense vector space, suitable for tasks such as clustering or semantic search.
Text Embedding
A
arinze
87
0
Sdg Sentence 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 and semantic search.
Text Embedding
Transformers

S
peter2000
13
0
S BlueBERT
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 and semantic search.
Text Embedding
Transformers

S
menadsa
58
0
Setfit Stance Prediction Spanish News Headlines
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
S
IsaacRodgz
13
0
Raw 2 No 0 Test 2 New.model
This is a sentence embedding model based on sentence-transformers, which maps text to a 768-dimensional vector space, suitable for tasks such as semantic search and text similarity calculation.
Text Embedding
Transformers

R
Wheatley961
13
0
Erlangshen SimCSE 110M Chinese
Apache-2.0
A Chinese sentence vector representation model based on the unsupervised version of SimCSE, trained with supervised contrastive learning using Chinese NLI data
Text Embedding
Transformers Chinese

E
IDEA-CCNL
186
21
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