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
Download result/acge_text_embedding_float/EcomRetrieval.json from aspire/acge_text_embedding: direct link, hf CLI and curl.
- Browser
- Download file 992 Bytes
-
https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F3/result/acge_text_embedding_float/EcomRetrieval.json
- Command line
-
hf download hf://aspire/acge_text_embedding@refs/pr/3/result/acge_text_embedding_float/EcomRetrieval.json
-
curl -L -o EcomRetrieval.json https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F3/result/acge_text_embedding_float/EcomRetrieval.json
992 Bytes
| { | |
| "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" | |
| } |