Sentence Similarity
sentence-transformers
PyTorch
Safetensors
bert
mteb
feature-extraction
Eval Results (legacy)
text-embeddings-inference
Instructions to use aspire/acge_text_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aspire/acge_text_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aspire/acge_text_embedding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 269 Bytes
4ec0bf2 | 1 2 3 4 5 6 7 8 9 10 | {
"dataset_revision": "5798586b105c0434e4f0fe5e767abe619442cf93",
"mteb_dataset_name": "ThuNewsClusteringP2P",
"mteb_version": "1.1.2",
"test": {
"evaluation_time": 519.44,
"v_measure": 0.7513656333926841,
"v_measure_std": 0.015941767332697955
}
} |