BGE-m3-ko

388.0K
63
8K
GPT-3 class
558M
2 languages
license:apache-2.0
by
dragonkue
Embedding Model
OTHER
Good
388K downloads
Production-ready
Edge AI:
Mobile
Laptop
Server
2GB+ RAM
Mobile
Laptop
Server
Quick Summary

--- language: - ko - en library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_p...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Code Examples

Usagebash
pip install -U sentence-transformers
Usagepython
from sentence_transformers import SentenceTransformer

# Download from the ๐Ÿค— Hub
model = SentenceTransformer("dragonkue/bge-m3-ko")
# Run inference
sentences = [
    '์ˆ˜๊ธ‰๊ถŒ์ž ์ค‘ ๊ทผ๋กœ ๋Šฅ๋ ฅ์ด ์—†๋Š” ์ž„์‚ฐ๋ถ€๋Š” ๋ช‡ ์ข…์— ํ•ด๋‹นํ•˜๋‹ˆ?',
    '๋‚ด๋…„๋ถ€ํ„ฐ ์ €์†Œ๋“์ธต 1์„ธ ๋ฏธ๋งŒ ์•„๋™์˜ \n์˜๋ฃŒ๋น„ ๋ถ€๋‹ด์ด ๋” ๋‚ฎ์•„์ง„๋‹ค!\n์˜๋ฃŒ๊ธ‰์—ฌ์ œ๋„ ๊ฐœ์š”\nโ–ก (๋ชฉ์ ) ์ƒํ™œ์œ ์ง€ ๋Šฅ๋ ฅ์ด ์—†๊ฑฐ๋‚˜ ์ƒํ™œ์ด ์–ด๋ ค์šด ๊ตญ๋ฏผ๋“ค์—๊ฒŒ ๋ฐœ์ƒํ•˜๋Š” ์งˆ๋ณ‘, ๋ถ€์ƒ, ์ถœ์‚ฐ ๋“ฑ์— ๋Œ€ํ•ด ๊ตญ๊ฐ€๊ฐ€ ์˜๋ฃŒ์„œ๋น„์Šค ์ œ๊ณต\nโ–ก (์ง€์›๋Œ€์ƒ) ๊ตญ๋ฏผ๊ธฐ์ดˆ์ƒํ™œ๋ณด์žฅ ์ˆ˜๊ธ‰๊ถŒ์ž, ํƒ€ ๋ฒ•์— ์˜ํ•œ ์ˆ˜๊ธ‰๊ถŒ์ž ๋“ฑ\n\n| ๊ตฌ๋ถ„ | ๊ตญ๋ฏผ๊ธฐ์ดˆ์ƒํ™œ๋ณด์žฅ๋ฒ•์— ์˜ํ•œ ์ˆ˜๊ธ‰๊ถŒ์ž | ๊ตญ๋ฏผ๊ธฐ์ดˆ์ƒํ™œ๋ณด์žฅ๋ฒ• ์ด์™ธ์˜ ํƒ€ ๋ฒ•์— ์˜ํ•œ ์ˆ˜๊ธ‰๊ถŒ์ž |\n| --- | --- | --- |\n| 1์ข… | โ—‹ ๊ตญ๋ฏผ๊ธฐ์ดˆ์ƒํ™œ๋ณด์žฅ ์ˆ˜๊ธ‰๊ถŒ์ž ์ค‘ ๊ทผ๋กœ๋Šฅ๋ ฅ์ด ์—†๋Š” ์ž๋งŒ์œผ๋กœ ๊ตฌ์„ฑ๋œ ๊ฐ€๊ตฌ - 18์„ธ ๋ฏธ๋งŒ, 65์„ธ ์ด์ƒ - 4๊ธ‰ ์ด๋‚ด ์žฅ์• ์ธ - ์ž„์‚ฐ๋ถ€, ๋ณ‘์—ญ์˜๋ฌด์ดํ–‰์ž ๋“ฑ | โ—‹ ์ด์žฌ๋ฏผ(์žฌํ•ด๊ตฌํ˜ธ๋ฒ•) โ—‹ ์˜์ƒ์ž ๋ฐ ์˜์‚ฌ์ž์˜ ์œ ์กฑโ—‹ ๊ตญ๋‚ด ์ž…์–‘๋œ 18์„ธ ๋ฏธ๋งŒ ์•„๋™โ—‹ ๊ตญ๊ฐ€์œ ๊ณต์ž ๋ฐ ๊ทธ ์œ ์กฑโ€ค๊ฐ€์กฑโ—‹ ๊ตญ๊ฐ€๋ฌดํ˜•๋ฌธํ™”์žฌ ๋ณด์œ ์ž ๋ฐ ๊ทธ ๊ฐ€์กฑโ—‹ ์ƒˆํ„ฐ๋ฏผ(๋ถํ•œ์ดํƒˆ์ฃผ๋ฏผ)๊ณผ ๊ทธ ๊ฐ€์กฑโ—‹ 5โ€ค18 ๋ฏผ์ฃผํ™”์šด๋™ ๊ด€๋ จ์ž ๋ฐ ๊ทธ ์œ ๊ฐ€์กฑโ—‹ ๋…ธ์ˆ™์ธ โ€ป ํ–‰๋ คํ™˜์ž (์˜๋ฃŒ๊ธ‰์—ฌ๋ฒ• ์‹œํ–‰๋ น) |\n| 2์ข… | โ—‹ ๊ตญ๋ฏผ๊ธฐ์ดˆ์ƒํ™œ๋ณด์žฅ ์ˆ˜๊ธ‰๊ถŒ์ž ์ค‘ ๊ทผ๋กœ๋Šฅ๋ ฅ์ด ์žˆ๋Š” ๊ฐ€๊ตฌ | - |\n',
    '์ด์–ด ์ด๋‚  ์˜คํ›„ 1์‹œ30๋ถ„๋ถ€ํ„ฐ ์—ด๋ฆด ์˜ˆ์ •์ด๋˜ ์Šค๋…ธ๋ณด๋“œ ์—ฌ์ž ์Šฌ๋กœํ”„์Šคํƒ€์ผ ์˜ˆ์„  ๊ฒฝ๊ธฐ๋Š” ์—ฐ๊ธฐ๋ฅผ ๊ฑฐ๋“ญํ•˜๋‹ค ์ทจ์†Œ๋๋‹ค. ์กฐ์ง์œ„๋Š” ์˜ˆ์„  ์—†์ด ๋‹ค์Œ ๋‚  ๊ฒฐ์„ ์—์„œ ์ฐธ๊ฐ€์ž 27๋ช…์ด ํ•œ๋ฒˆ์— ๊ฒฝ๊ธฐํ•ด ์ˆœ์œ„๋ฅผ ๊ฐ€๋ฆฌ๊ธฐ๋กœ ํ–ˆ๋‹ค.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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