Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:21620
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use meandyou200175/E5_v4_instruct_topic_continue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use meandyou200175/E5_v4_instruct_topic_continue with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("meandyou200175/E5_v4_instruct_topic_continue") sentences = [ "task: classification | query: hope", "Khác", "Lễ hội", "Khác" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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