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:
- 6d7f878dc7cff21ed3cf4143f3f6d30fb8add7783845453d7dde33296b80f39e
- Size of remote file:
- 2.27 GB
- SHA256:
- 7796a2a71dd43d0034d3b4a1b0ce68f2e85c6c43c1d607a57c515e83131ac452
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