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
| { | |
| "dataset_revision": "58c2597a5943a2ba48f4668c3b90d796283c5639", | |
| "dev": { | |
| "evaluation_time": 150.3, | |
| "map_at_1": 0.594, | |
| "map_at_10": 0.69188, | |
| "map_at_100": 0.69708, | |
| "map_at_1000": 0.69724, | |
| "map_at_3": 0.67717, | |
| "map_at_5": 0.68762, | |
| "mrr_at_1": 0.593, | |
| "mrr_at_10": 0.69138, | |
| "mrr_at_100": 0.69658, | |
| "mrr_at_1000": 0.69674, | |
| "mrr_at_3": 0.67667, | |
| "mrr_at_5": 0.68712, | |
| "ndcg_at_1": 0.594, | |
| "ndcg_at_10": 0.73279, | |
| "ndcg_at_100": 0.75557, | |
| "ndcg_at_1000": 0.75968, | |
| "ndcg_at_3": 0.70339, | |
| "ndcg_at_5": 0.72207, | |
| "precision_at_1": 0.594, | |
| "precision_at_10": 0.0858, | |
| "precision_at_100": 0.00959, | |
| "precision_at_1000": 0.00099, | |
| "precision_at_3": 0.25967, | |
| "precision_at_5": 0.1648, | |
| "recall_at_1": 0.594, | |
| "recall_at_10": 0.858, | |
| "recall_at_100": 0.959, | |
| "recall_at_1000": 0.991, | |
| "recall_at_3": 0.779, | |
| "recall_at_5": 0.824 | |
| }, | |
| "mteb_dataset_name": "VideoRetrieval", | |
| "mteb_version": "1.1.2" | |
| } |