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: 1,016 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": "8731a845f1bf500a4f111cf1070785c793d10e64",
"dev": {
"evaluation_time": 710.04,
"map_at_1": 0.27062,
"map_at_10": 0.75296,
"map_at_100": 0.79104,
"map_at_1000": 0.79177,
"map_at_3": 0.5305,
"map_at_5": 0.65118,
"mrr_at_1": 0.88813,
"mrr_at_10": 0.91716,
"mrr_at_100": 0.91808,
"mrr_at_1000": 0.91812,
"mrr_at_3": 0.91206,
"mrr_at_5": 0.91542,
"ndcg_at_1": 0.88813,
"ndcg_at_10": 0.83269,
"ndcg_at_100": 0.87261,
"ndcg_at_1000": 0.8794,
"ndcg_at_3": 0.84679,
"ndcg_at_5": 0.83247,
"precision_at_1": 0.88813,
"precision_at_10": 0.41293,
"precision_at_100": 0.04994,
"precision_at_1000": 0.00516,
"precision_at_3": 0.73936,
"precision_at_5": 0.61901,
"recall_at_1": 0.27062,
"recall_at_10": 0.8233,
"recall_at_100": 0.95064,
"recall_at_1000": 0.98467,
"recall_at_3": 0.54865,
"recall_at_5": 0.68841
},
"mteb_dataset_name": "T2Retrieval",
"mteb_version": "1.1.2"
} |