Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2436
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-search-mini-v1-1-mnrl-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-search-mini-v1-1-mnrl-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-search-mini-v1-1-mnrl-sts") sentences = [ "Persentase penduduk usia 15-24 tahun di Kota Bandar Lampung yang tidak sekolah dan tidak bekerja (NEET) adalah 10%.", "Lembaga layanan menerima sekitar sepuluh ribu pengaduan kekerasan terhadap perempuan pada 2023.", "Volume sampah plastik yang dihasilkan Kota Bandar Lampung setiap hari mencapai 100 ton.", "Komponen volatile foods mengalami deflasi 0,5 persen secara bulanan pada Mei 2025." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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