Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:212930
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use yahyaabd/allstats-semantic-search-model-v1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yahyaabd/allstats-semantic-search-model-v1-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yahyaabd/allstats-semantic-search-model-v1-2") sentences = [ "analisis perekonomian indonesia triwulan i 2007", "Indikator Ekonomi Februari 2017", "Perkembangan Harga Produsen Gabah Maret 2021", "Hasil Survei Komoditas Perikanan Potensi 2021 Profil Rumah Tangga Usaha Budidaya Rumput Laut" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 296 Bytes
1e0198b | 1 2 3 4 5 6 7 8 9 10 | {
"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
} |