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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 4, | |
| "name": "4", | |
| "path": "4_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |