Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:10998
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2-mnrl-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2-mnrl-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/bps-custom-tokenizer-paraphrase-multilingual-MiniLM-L12-v2-mnrl-2") sentences = [ "Laporan neraca arus dana dalam Rupiah miliar untuk Q2 2011", "Ringkasan Neraca Arus Dana, Triwulan II, 2011*), (Miliar Rupiah)", "Tingkat Inflasi Harga Konsumen Nasional Tahun Kalender (Y-to-D) 1 (2022=100)", "Jumlah Pendapatan Setelah Pajak Menurut Golongan Rumah Tangga (miliar rupiah), 2000, 2005, dan 2008" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1900c2e04319a0dab29e6ca464a68a3a3110a99c5084f066c66eae3f323413d3
- Size of remote file:
- 17.3 MB
- SHA256:
- d8ae59c2832bac5ed7b8eb6d03aaf0710ad644d53c6a85566435055af2d7fbc4
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