Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:2609
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use TextModel/Embedding-crime-indo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TextModel/Embedding-crime-indo with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TextModel/Embedding-crime-indo") sentences = [ "query: Kalau si koruptor ternyata udah nggak punya harta lagi buat bayar uang pengganti, apa konsekuensinya?", "passage: Hukumnya adalah tindak pidana yang diancam dengan pidana penjara paling lama 4 tahun atau pidana denda paling banyak kategori IV karena menggunakan ancaman kekerasan. (Pasal 302 KUHP)", "passage: Kalau harta bendanya tidak mencukupi, terpidana bisa dipidana penjara yang lamanya tidak melebihi ancaman maksimum pidana pokoknya dan sudah ditentukan langsung di dalam putusan pengadilan.", "passage: Penyitaan dan pelelangan harta bila uang pengganti tidak dibayar." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 1,637 Bytes
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"architectures": [
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"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
"rope_parameters": {
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