Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:37302
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/paraphrase-multilingual-miniLM-L12-V2-ocl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/paraphrase-multilingual-miniLM-L12-V2-ocl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/paraphrase-multilingual-miniLM-L12-V2-ocl") sentences = [ "Pekerja 15+ Berdasarkan Status Pekerjaan & Pendidikan, 1997-2007", "Indeks Harga Konsumen Menurut Kelompok Pengeluaran, 2020-2023", "Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Riau, 2018-2023", "Posisi Utang Luar Negeri Pemerintah dan Bank Sentral Menurut Kategori Kreditor dan Persyaratan Kredit (juta US$), 2005-2015" ] 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" | |
| } | |
| ] |