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: 992 Bytes
4ec0bf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | {
"dataset_revision": "687de13dc7294d6fd9be10c6945f9e8fec8166b9",
"dev": {
"evaluation_time": 148.99,
"map_at_1": 0.521,
"map_at_10": 0.6264,
"map_at_100": 0.63127,
"map_at_1000": 0.63144,
"map_at_3": 0.603,
"map_at_5": 0.61785,
"mrr_at_1": 0.521,
"mrr_at_10": 0.6264,
"mrr_at_100": 0.63127,
"mrr_at_1000": 0.63144,
"mrr_at_3": 0.603,
"mrr_at_5": 0.61785,
"ndcg_at_1": 0.521,
"ndcg_at_10": 0.67604,
"ndcg_at_100": 0.69958,
"ndcg_at_1000": 0.70343,
"ndcg_at_3": 0.62876,
"ndcg_at_5": 0.65553,
"precision_at_1": 0.521,
"precision_at_10": 0.0831,
"precision_at_100": 0.00941,
"precision_at_1000": 0.00097,
"precision_at_3": 0.23433,
"precision_at_5": 0.1536,
"recall_at_1": 0.521,
"recall_at_10": 0.831,
"recall_at_100": 0.941,
"recall_at_1000": 0.97,
"recall_at_3": 0.703,
"recall_at_5": 0.768
},
"mteb_dataset_name": "EcomRetrieval",
"mteb_version": "1.1.2"
} |