Gte Base Ko
This is a sentence-transformers model fine-tuned on a Korean triplet dataset based on Alibaba NLP/gte-multilingual-base, designed for semantic textual similarity tasks.
Text Embedding
Safetensors Supports Multiple Languages#Korean sentence similarity#Multilingual embeddings#Long text processing
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Release Time : 11/17/2024
Model Overview
The model maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks like semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.
Model Features
Multilingual base model
Based on Alibaba NLP/gte-multilingual-base, offering strong multilingual processing capabilities
Korean optimization
Fine-tuned on a Korean triplet dataset, making it particularly suitable for Korean text processing
High accuracy
Achieves a cosine accuracy of 0.9855 on the development set
Long text support
Supports sequences up to 8192 tokens, ideal for processing long texts
Model Capabilities
Semantic textual similarity calculation
Semantic search
Text feature extraction
Text clustering
Text classification
Use Cases
Information retrieval
Similar document retrieval
Find semantically similar documents based on query text
High-accuracy similarity matching
Content recommendation
Related content recommendation
Recommend semantically similar content based on user browsing history
Enhances user engagement and content discovery efficiency
๐ SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a sentence-transformers model fine - tuned from Alibaba-NLP/gte-multilingual-base on the nlpai-lab/ko-triplet-v1.0 dataset. It maps sentences and paragraphs to a 768 - dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
๐ Quick Start
Prerequisites
First, make sure you have installed the Sentence Transformers library. If not, you can install it using the following command:
pip install -U sentence-transformers
Running Inference
After the installation, you can load the model and run inference as follows:
from sentence_transformers import SentenceTransformer
# Download from the ๐ค Hub
model = SentenceTransformer("scottsuk0306/gte-base-ko")
# Run inference
sentences = [
'์ค, ์ธ์ฌํ๋ค, ์์ฃผํ๋ค, ์น๋ค, ์ฌ๋, ๋ชฉํ',
'๊ทธ ์ค์ ์์ฃผํ ์ฌ๋์๊ฒ ๋ชฉํ์ ์น๋ฉฐ ์ธ์ฌ๋ฅผ ํ๋ค.',
'์ฌ์ฃผ๋ผ๋ ๊ฒ์ ์์ , ์์
, ํ์๊ธฐ, ๊ธ์ฐ๊ธฐ, ๋งํ๊ธฐ, ๊ณ์ฐํ๊ธฐ๋ค.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
โจ Features
- Maps sentences and paragraphs to a 768 - dimensional dense vector space.
- Can be used for multiple NLP tasks such as semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering.
๐ฆ Installation
To use this model, you need to install the sentence-transformers
library. You can install it via pip
:
pip install -U sentence-transformers
๐ป Usage Examples
Basic Usage
from sentence_transformers import SentenceTransformer
# Download from the ๐ค Hub
model = SentenceTransformer("scottsuk0306/gte-base-ko")
# Run inference
sentences = [
'์ค, ์ธ์ฌํ๋ค, ์์ฃผํ๋ค, ์น๋ค, ์ฌ๋, ๋ชฉํ',
'๊ทธ ์ค์ ์์ฃผํ ์ฌ๋์๊ฒ ๋ชฉํ์ ์น๋ฉฐ ์ธ์ฌ๋ฅผ ํ๋ค.',
'์ฌ์ฃผ๋ผ๋ ๊ฒ์ ์์ , ์์
, ํ์๊ธฐ, ๊ธ์ฐ๊ธฐ, ๋งํ๊ธฐ, ๊ณ์ฐํ๊ธฐ๋ค.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
๐ Documentation
Model Details
Model Description
Property | Details |
---|---|
Model Type | Sentence Transformer |
Base model | Alibaba-NLP/gte-multilingual-base |
Maximum Sequence Length | 8192 tokens |
Output Dimensionality | 768 tokens |
Similarity Function | Cosine Similarity |
Training Dataset | nlpai-lab/ko-triplet-v1.0 |
Language | ko |
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
Evaluation
Metrics
Triplet
- Dataset:
dev
- Evaluated with
TripletEvaluator
Metric | Value |
---|---|
cosine_accuracy | 0.9855 |
dot_accuracy | 0.0145 |
manhattan_accuracy | 0.9855 |
euclidean_accuracy | 0.9855 |
max_accuracy | 0.9855 |
Training Details
Training Dataset
- nlpai-lab/ko-triplet-v1.0
- Dataset: nlpai-lab/ko-triplet-v1.0 at 9cc1d6a
- Size: 10,000 training samples
- Columns:
anchor
,positive
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 9 tokens
- mean: 22.12 tokens
- max: 146 tokens
- min: 10 tokens
- mean: 92.69 tokens
- max: 1815 tokens
- min: 8 tokens
- mean: 99.24 tokens
- max: 880 tokens
- Samples:
anchor positive negative ๊ธ์ฐ๊ธฐ, ๋ํ, ์ฐธ๊ฐ๋ฃ, ๋ฐ๋ค
๊ธ์ฐ๊ธฐ ๋ํ๋ ์ฐธ๊ฐ์ํํ ์ผ์ ๋ถ์ ์ฐธ๊ฐ๋ฃ๋ฅผ ๋ฐ์๋ค.
์ฌ์ฃผ๋ ์์ , ์์ , ํ์๊ธฐ, ๊ธ์ฐ๊ธฐ, ๋งํ๊ธฐ, ๊ณ์ฐํ๊ธฐ ๋ฑ๋ฑ ์ด๋ค.
"K๋ฆฌ๊ทธ 2002 ์์ฆ"์์ ๊ธฐ๋กํ ๊ด๊ฐ์ ์๋ณด๋ค ๋ ์ผ ๊ณ ํ๋ฆฌํ ๋ค์๋ฌ ๊ฒฝ๊ธฐ์ฅ์์ ๊ฐ์ต๋ "์ 4ํ ์ธ๊ณ ์ก์ ์ ์๊ถ ๋ํ"์ ๊ด๊ฐ ์๊ฐ ๋ง์๋?
1993๋ ์ธ๊ณ ์ก์ ์ ์๊ถ ๋ํ. ์ 4ํ ์ธ๊ณ ์ก์ ์ ์๊ถ ๋ํ๋ ๊ตญ์ ์ก์ ๊ฒฝ๊ธฐ ์ฐ๋งน ์ฃผ๊ด์ผ๋ก 1993๋ 8์ 13์ผ์์ 8์ 22์ผ๊น์ง ๋ ์ผ ์ํฌํธ๊ฐ๋ฅดํธ ๊ณ ํ๋ฆฌํ ๋ค์๋ฌ ๊ฒฝ๊ธฐ์ฅ์์ ์ด๋ฆฐ ๊ตญ์ ์ก์ ๋ํ์ด๋ค. ๋ ์ผ์์ ์ด๋ฆฐ ์ฒซ ๋ฒ์งธ ์ธ๊ณ ์ก์ ์ ์๊ถ ๋ํ์๋ค. 187๊ฐ๊ตญ ์ ์ 1630๋ช ์ด ์ฐธ๊ฐํ์ผ๋ฉฐ, ๋ํ ์ญ์ฌ์ ๊ฐ์ฅ ๋ง์ ์์ธ 58๋ง 5000๋ช ์ ๊ด์ค์ด ์ ์ฅํ๋ค.
์ก๊ฐํธ. ๊ฒฝ๋ ฅ. 1996-2000: ์ด๊ธฐ ๊ฒฝ๋ ฅ๊ณผ ์ฃผ๋ชฉ ๋ฐ๋ค. 1999๋ ์๋ ๊ฐ์ ๊ท ๊ฐ๋ ์ ์ํ ใ์ฌ๋ฆฌใ์ ์ด์ฅ๊ธธ ์ญํ ๋ก ์ถ์ฐํ๋ค. ์ด ์ํ๋ ๊ด๊ฐ์ 582๋ง ๋ช ์ ๊ธฐ๋กํ๋ฉฐ ๋น์ ๊ตญ๋ด ์ต๋ค ๊ด๊ฐ ์ํ ๊ธฐ๋ก์ ๊ฐฑ์ ํ๊ณ , ์ต์ด๋ก 500๋ง ๊ด๊ฐ์ ๋์ด์ฐ๋ค. 2000๋ ์๋ ์ฝ๋ฏธ๋ ์ํ ใ๋ฐ์น์ใ์์ ์ฒซ ์ฃผ์ฐ์ ๋งก์๋ค. ์ด ์ํ์์ ๊ทธ๋ ์ํ์์ด์ ๋ ์ฌ๋ฌ ๋ํธ ์ญํ ๋ก ์บ์คํ ๋์ด ์ํ๋ฅผ ์ํด ๋ ์ฌ๋ง ํ๋ จ์ ํ์๋ค. ์ดํ ์ก๊ฐํธ๋ ์ด ์ํ๊ฐ ๊ฐ์ฅ ์ธ์ ๊น๋ค๊ณ ๊ผฝ์ผ๋ฉฐ "๋ฌผ๋ฆฌ์ ์ผ๋ก ๊ฐ์ฅ ๊ทนํ์ ์ํฉ๊น์ง ๊ฐ ์ํ์ด์๋ค๋ ์๊ฐ์ด ๋ ๋ค. ๋ง์ฝ ์ง๊ธ ๋ ์ฌ๋ง์ ๋ค์ ํ๋ค๋ฉด ์ฃฝ์ ๊ฑฐ๋ค"๋ผ๊ณ ๋งํ๋ค. ๊ฐ์ ํด ๊ทธ๋ ๋ฐ์ฐฌ์ฑ ๊ฐ๋ ์ ์ํ ใ๊ณต๋๊ฒฝ๋น๊ตฌ์ญ JSAใ์์ ์ค๊ฒฝํ ์ค์ฌ ์ญํ ๋ก ์ถ์ฐํ๋ค. ์ด ์ํ๋ ์ด์ ๊น์ง ๋ฐ๊ณต์ด๋ฐ์ฌ๋ก๊ธฐ์์ ๋ฒ์ด๋์ง ๋ชปํ๋ ๋ถ๋จ ์์ฌ ํ๊ตญ์ํ์ ์๊ฐ์ ์ธ๊ฐ์ ๋ก ํ์ฅํ ์ํ์ผ๋ก 583๋ง ๋ช ์ ๊ด๊ฐ์๋ฅผ ๊ธฐ๋กํ๋ฉฐ ์ญ๋ ํฅํ 1์ ์ํ๋ก ๊ธฐ๋กํ๋ค. ์ก๊ฐํธ๋ 2019๋ ๋งค์ฒด์์ ์ธํฐ๋ทฐ์์ ๋ฐฐ์ฐ ์ธ์์ ์ ํ์ ์ด ๋๋ค๊ณ ์ธ๊ธํ๋ฉฐ โ๋ ์ํ๊ฐ ๊ฐ๋ดํ 2000๋ ์ ๋ฐฐ์ฐ ์ํ ์ด๋ฐ์ ๋ถ๊ธฐ์ ์ด ๋๋คโ๊ณ ๋งํ๋ค. ํ ์ํ ๊ด๊ณ์๋ ์ก๊ฐํธ์๊ฒ "ใ๋ฐ์น์ใ์์ ๋ณด์ฌ์ค๋ฐ์ด๋ ์ฐ๊ธฐ๋ฅผ ๋ณด๊ณ ๊ทธ ์ด์์ ์ฐ๊ธฐ๋ ๋์ฌ ์ ์์ ๊ฒ์ด๋ผ ์๊ฐํ๋ค. ์ด๋ฒ ์ํ๋ฅผ ๋ณด๊ณ ๋ด๊ฐ ๋น์ ์ฐ๊ธฐ์ ํ๊ณ๋ฅผ ๋๋ฌด ๋ฎ๊ฒ ์ก์์์ ์์๋ค"๊ณ ๋งํ๋ค. ใ๋งค์ผ๊ฒฝ์ ใ์ ํ๋ฆฌ๋ทฐ์์๋ "์ก๊ฐํธ๊ฐ ๊ทธ๋ ค๋ธ ๋ฐ๋ปํ๊ณ ๋๋ํ ์ค๊ฒฝํ ์ค์ฌ๋ ๊ตฐ๊ธฐ๊ฐ ํ์ฐธ ๋น ์ ธ ์๋ก ๋ ธ๋ฅ๊ฑฐ๋ฆฌ๋ ๊ฒ์ผ๋ก ๋น์น ์๋ ์๋ ๋ชจ์ต๋ค์ ๋๋ฌผ๋๋ ํ์ ์ ๋ก ์ก์์ฃผ๋ ๋ ๋ ํ ๋ฐ์นจ๋๋ค"๋ผ๊ณ ๋ฆฌ๋ทฐํ๋ค. ์ด๋ฌํ ํธํ ์์ ์ 1ํ ๋ถ์ฐ์ํํ๋ก ๊ฐํํ์, ์ 38ํ ๋์ข ์์ํ์ , ์ 3ํ ๋๋น์์์์ํ์ , ์์ ๋จ์ฐ์ฃผ์ฐ์์ ์์ํ๊ณ , ์ด ์ธ์๋ ๋ฐฑ์์์ ๋์์์ ์ธ๊ธฐ์์ ๋ฐ๋ ๋ฑ ๋ค์์ ์์์์์ ์ฐ๊ธฐ๋ ฅ๊ณผ ์คํ์ฑ์์ ์ธ์ ์ ๋ฐ์๋ค.
ํธ๋ฆฌ๊ฑฐ๋ ๋ฐ์ดํฐ๋ฅผ ์ ์ฅ์์ผ?
ํธ๋ฆฌ๊ฑฐ๋ ์ํ ๋ ์ํ ์ฅ๋น๋ฅผ ๊ตฌ๋์์ผ ์๋๋ฆฌ์ค๋ฅผ ๋ฐ์์ํค๋ฉฐ, ์๋๋ฆฌ์ค์ ๋ฐ๋ผ ์ํ ๋๋ถ์ ์ก์ ๊ธฐ์ ์์ ๊ธฐ๋ฅผ ์ ์ด ๋ฐ ์ธก์ ํ์ฌ ๋ฐ์ดํฐ๋ฅผ ์ ์ฅํ ๊ฒ ๋๋ค.
์ํ๊ด์์๋ ์ ๋๋ถ๋ถ์ ๊ด๊ฐ์ด ์ค๊ฐ๋๊ฒ ์๋ฆฌ๊ฐ ์ ๋ค๋ฆฌ๋ ๊ฑธ๊น? ๋์์ธ์ด Uglyํ์ง๋ง ์ CRT์ผ๋๋ ์๋ฆฌ๊ฐ
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