Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:73392
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-semantic-mpnet-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-semantic-mpnet-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-semantic-mpnet-v2") sentences = [ "Berapa persen kenaikan Indeks Harga Perdagangan Besar (IHPB) Umum Nasional pada bulan April 2021?", "Statistik Kriminal 2023", "Ekonomi Indonesia Triwulan I-2021 turun 0,74 persen (y-on-y)", "Survei Biaya Hidup (SBH) 2018 Ambon dan Tual" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 205 Bytes
2cdb99a | 1 2 3 4 5 6 7 8 9 10 | {
"__version__": {
"sentence_transformers": "3.3.1",
"transformers": "4.48.0",
"pytorch": "2.4.1+cu121"
},
"prompts": {},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |