Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:8100
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use AlIshaq/E5-faq-pesantren with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AlIshaq/E5-faq-pesantren with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AlIshaq/E5-faq-pesantren") sentences = [ "Apa visi dari PPS. Imam Syafi'i?", "Ya, ada forum diskusi adab yang dibimbing ustadz setiap pekan.", "Menjadi Madrasah Diniyah yang unggul dalam mewujudkan santri yang bertaqwa, ber-akhlak, dan kompetitif pada akademik, terutama di bidang tahfizh Qur'an.", "Ya, diadakan rapat guru mingguan dan bulanan." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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Model tree for AlIshaq/E5-faq-pesantren
Base model
intfloat/multilingual-e5-smallEvaluation results
- Pearson Cosine on evalself-reportedNaN
- Spearman Cosine on evalself-reportedNaN