Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:25580
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-search-miniLM-v1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-search-miniLM-v1-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-search-miniLM-v1-2") sentences = [ "ikhtisar arus kas triwulan 1, 2004 (miliar)", "Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005", "Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau Jawa dan Sumatera dengan Nasional (2018=100)", "Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tengah, 2018-2023" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |