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/ATEC.json from aspire/acge_text_embedding: direct link, hf CLI and curl.
- Browser
- Download file 468 Bytes
-
https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F2/result/acge_text_embedding_float/ATEC.json
- Command line
-
hf download hf://aspire/acge_text_embedding@refs/pr/2/result/acge_text_embedding_float/ATEC.json
-
curl -L -o ATEC.json https://huggingface.co/aspire/acge_text_embedding/resolve/refs%2Fpr%2F2/result/acge_text_embedding_float/ATEC.json
468 Bytes
| { | |
| "dataset_revision": "0f319b1142f28d00e055a6770f3f726ae9b7d865", | |
| "mteb_dataset_name": "ATEC", | |
| "mteb_version": "1.1.2", | |
| "test": { | |
| "cos_sim": { | |
| "pearson": 0.535259991525419, | |
| "spearman": 0.5794530657917338 | |
| }, | |
| "euclidean": { | |
| "pearson": 0.6116990157908463, | |
| "spearman": 0.5794530432728487 | |
| }, | |
| "evaluation_time": 32.44, | |
| "manhattan": { | |
| "pearson": 0.6116784257779159, | |
| "spearman": 0.579444599262835 | |
| } | |
| } | |
| } |