Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:20554
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use HeyDunaX/Tay_Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HeyDunaX/Tay_Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HeyDunaX/Tay_Embedding") sentences = [ "bon", "cây mon", "đổ chậu nước", "yên phận làm ăn" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81500d5fa42f2b56d8dcc8894672b02bc7c98f96de5f572cbded458f27e8bec2
- Size of remote file:
- 16.8 MB
- SHA256:
- bc552f2faa9ccaf0be4136806b77d442c6f81061a20ce3c569927d3908599284
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