Sentence Similarity
sentence-transformers
Safetensors
Indonesian
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:6198
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ewideplus/indoedu-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ewideplus/indoedu-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ewideplus/indoedu-e5-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 399d3479d48a590eba51131322da06996ec33fd7b8b8dc6a9c2a8c8d1e1ccdaa
- Size of remote file:
- 1.11 GB
- SHA256:
- e800541c2b25e35f1fdeb9e3d244a52a0ebe55dae7b0e89e525308d18b9c1777
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.