Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:121
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use qygoh/ilo-embedding-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use qygoh/ilo-embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("qygoh/ilo-embedding-model") sentences = [ "Kasano a mausar ti online a panag-apply iti tulong dagiti Golden Citizens?", "Ania dagiti addang a mangaplikar iti tulong kadagiti umili babaen ti online system?", "Ania ti pamay-an a nalaklaka a mangasaba iti tulong kadagiti umili?", "Ania dagiti addang a mabalin nga aramiden tapno maaddaan iti status ti binulan a sueldo iti agdama a tawen?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73a563baf68d51e5cf2e28fc8a7d5887633303c074a7029c1fdcd02a0c8ef954
- Size of remote file:
- 17.1 MB
- SHA256:
- e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.