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_bf16/CLSClusteringP2P.json from aspire/acge_text_embedding: direct link, hf CLI and curl.
- Browser
- Download file 266 Bytes
-
https://huggingface.co/aspire/acge_text_embedding/resolve/main/result/acge_text_embedding_bf16/CLSClusteringP2P.json
- Command line
-
hf download hf://aspire/acge_text_embedding/result/acge_text_embedding_bf16/CLSClusteringP2P.json
-
curl -L -o CLSClusteringP2P.json https://huggingface.co/aspire/acge_text_embedding/resolve/main/result/acge_text_embedding_bf16/CLSClusteringP2P.json
266 Bytes
| { | |
| "dataset_revision": "4b6227591c6c1a73bc76b1055f3b7f3588e72476", | |
| "mteb_dataset_name": "CLSClusteringP2P", | |
| "mteb_version": "1.1.2", | |
| "test": { | |
| "evaluation_time": 184.69, | |
| "v_measure": 0.46546853871797733, | |
| "v_measure_std": 0.014569739407015586 | |
| } | |
| } |