Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:88250
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-semantic-mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-semantic-mpnet with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-semantic-mpnet") sentences = [ "Laporan ekspor Indonesia Juli 2020", "Statistik Produksi Kehutanan 2021", "Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Juli 2020", "Statistik Politik 2017" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 205 Bytes
f6ba240 | 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"
} |