Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2602
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-ir-mpnet-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-ir-mpnet-base-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-ir-mpnet-base-v1") sentences = [ "Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah (triliun) 2010", "Nilai Ekspor Menurut Pelabuhan Utama (Nilai FOB: juta US$) 2000-2023", "Suhu Minimum, Rata-Rata, dan Maksimum di Stasiun Pengamatan BMKG (oC), 2011-2015", "Nilai Ekspor Menurut Negara Tujuan Utama (Nilai FOB: juta US$), 2000-2023" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |