Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
albert
feature-extraction
Generated from Trainer
dataset_size:6399
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use yosriku/exp_data_scale_5files with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yosriku/exp_data_scale_5files with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yosriku/exp_data_scale_5files") sentences = [ "Apa yang dilakukan wisatawan?", "The number of tourists visiting during the 2018 holiday reached 9,870 people in one day. Every activity of tourists will produce waste in the tourist area, especially organic wast e. Organic waste has good energy potential", "listrik yang dihasilkan dari proses gasifikasi yang memiliki nilai efisiensi 11% adalah 6,38 kW atau 6.380 Watt. Resume perhitungan analisis potensi energi listrik dari sampah organik yang siap diproses dapat dilihat pada Tabel 3. Tabel 3.", "Huruf e Cukup jelas. Huruf f Yang dimaksud dengan alat bukti lain, meliputi, informasi yang diucapkan, dikirimkan, diterima, ata u disimpan secara elektronik, magnetik, optik, dan/at au yang serupa dengan itu;" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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