Fine Tuned E5 Small Drugproduct

91
8
1 language
license:mit
by
Yoonyoul
Embedding Model
OTHER
New
91 downloads
Early-stage
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Quick Summary

๐Ÿงฌ Fine-tuned E5-small for Korean Drug Product Semantic Embedding ๐Ÿ“˜ Model Overview ์ด ๋ชจ๋ธ์€ intfloat/multilingual-e5-small ๊ธฐ๋ฐ˜์œผ๋กœ, ์˜์•ฝํ’ˆ ์š”์•ฝยท์ƒ์„ธ ๋ฐ์ดํ„ฐ(`drugsummary`, `dr...

Code Examples

๐Ÿง  Use Case Examplepython
from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("Yoonyoul/fine-tuned-e5-small-drugproduct")

query = "์—ด์„ ๋‚ด๋ฆฌ๋Š” ์•ฝ์€?"
docs = [
    "ํŒ์ฝœ์—์ด๋‚ด๋ณต์•ก์€ ํ•ด์—ด์ง„ํ†ต์ œ์ž…๋‹ˆ๋‹ค.",
    "๋งˆ์ด์•”๋ถ€ํ†จ์ •์€ ํ•ญ๊ฒฐํ•ต์ œ์ž…๋‹ˆ๋‹ค.",
    "์ง€๋ฅดํ…์ •์€ ํ•ญํžˆ์Šคํƒ€๋ฏผ์ œ์ž…๋‹ˆ๋‹ค."
]

emb_q = model.encode(query, convert_to_tensor=True)
emb_d = model.encode(docs, convert_to_tensor=True)

scores = util.cos_sim(emb_q, emb_d)[0]
for doc, score in zip(docs, scores):
    print(f"{doc} โ†’ ์œ ์‚ฌ๋„: {score.item():.4f}")

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