Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:123640
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-semantic-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-semantic-base-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-semantic-base-v1") sentences = [ "data perempuan dan laki-laki di indonesia 2022", "Statistik Telekomunikasi Indonesia 2012", "Perkembangan Indeks Produksi Triwulanan Industri Mikro dan Kecil 2023", "Pada Agustus 2014, Jumlah wisman mencapai 826,8 ribu" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9abfbcdb4ffc00d355a5aceeb3df3cad5ed9f079223b9434115e96b6423ffb1
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.