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/T2Retrieval.json from aspire/acge_text_embedding: direct link, hf CLI and curl.
- Browser
- Download file 1.02 kB
-
https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F2/result/acge_text_embedding_float/T2Retrieval.json
- Command line
-
hf download hf://aspire/acge_text_embedding@refs/pr/2/result/acge_text_embedding_float/T2Retrieval.json
-
curl -L -o T2Retrieval.json https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F2/result/acge_text_embedding_float/T2Retrieval.json
1.02 kB
| { | |
| "dataset_revision": "8731a845f1bf500a4f111cf1070785c793d10e64", | |
| "dev": { | |
| "evaluation_time": 3170.56, | |
| "map_at_1": 0.27066, | |
| "map_at_10": 0.75299, | |
| "map_at_100": 0.79106, | |
| "map_at_1000": 0.79179, | |
| "map_at_3": 0.53054, | |
| "map_at_5": 0.65121, | |
| "mrr_at_1": 0.88813, | |
| "mrr_at_10": 0.91715, | |
| "mrr_at_100": 0.91807, | |
| "mrr_at_1000": 0.9181, | |
| "mrr_at_3": 0.91205, | |
| "mrr_at_5": 0.91541, | |
| "ndcg_at_1": 0.88813, | |
| "ndcg_at_10": 0.83271, | |
| "ndcg_at_100": 0.87262, | |
| "ndcg_at_1000": 0.87941, | |
| "ndcg_at_3": 0.84681, | |
| "ndcg_at_5": 0.83248, | |
| "precision_at_1": 0.88813, | |
| "precision_at_10": 0.41294, | |
| "precision_at_100": 0.04994, | |
| "precision_at_1000": 0.00516, | |
| "precision_at_3": 0.73939, | |
| "precision_at_5": 0.61901, | |
| "recall_at_1": 0.27066, | |
| "recall_at_10": 0.82331, | |
| "recall_at_100": 0.95065, | |
| "recall_at_1000": 0.98467, | |
| "recall_at_3": 0.54868, | |
| "recall_at_5": 0.68841 | |
| }, | |
| "mteb_dataset_name": "T2Retrieval", | |
| "mteb_version": "1.1.2" | |
| } |