Sentence Similarity
sentence-transformers
Safetensors
multilingual
new
feature-extraction
Generated from Trainer
dataset_size:217680
loss:MultipleNegativesRankingLoss
custom_code
text-embeddings-inference
Instructions to use arulpm/gte-ipb-clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use arulpm/gte-ipb-clinical with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("arulpm/gte-ipb-clinical", trust_remote_code=True) sentences = [ "Keluhan: bintik2 merah di badan. 3 hari\r\ngatal +\nAnamnesa Pemeriksaan Dokter: bintik2 merah di badan. 3 hari\r\ngatal +", "Obat: Devit 5000iu\nDeskripsi Obat: Vitamin D3 dosis tinggi untuk defisiensi", "Obat: Betaver 6 mg\nDeskripsi Obat: Obat untuk mengobati vertigo dan gangguan keseimbangan", "Obat: Captopril 12,5 mg\nDeskripsi Obat: Obat antihipertensi golongan ACE inhibitor dosis rendah untuk menurunkan tekanan darah" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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