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: 992 Bytes
6cbf52b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.2.3",
"transformers": "5.0.0",
"pytorch": "2.10.0+cu128"
},
"prompts": {
"query": "task: search result | query: ",
"document": "title: none | text: ",
"BitextMining": "task: search result | query: ",
"Clustering": "task: clustering | query: ",
"Classification": "task: classification | query: ",
"InstructionRetrieval": "task: code retrieval | query: ",
"MultilabelClassification": "task: classification | query: ",
"PairClassification": "task: sentence similarity | query: ",
"Reranking": "task: search result | query: ",
"Retrieval": "task: search result | query: ",
"Retrieval-query": "task: search result | query: ",
"Retrieval-document": "title: none | text: ",
"STS": "task: sentence similarity | query: ",
"Summarization": "task: summarization | query: "
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |