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": "1399c76144fd37290681b995c656ef9b2e06e26d", | |
| "mteb_dataset_name": "AmazonReviewsClassification", | |
| "mteb_version": "1.1.2", | |
| "test": { | |
| "evaluation_time": 48.04, | |
| "zh": { | |
| "accuracy": 0.4854200000000001, | |
| "accuracy_stderr": 0.016364217060403467, | |
| "f1": 0.4660313582582637, | |
| "f1_stderr": 0.016138610779200774, | |
| "main_score": 0.4854200000000001 | |
| } | |
| }, | |
| "validation": { | |
| "evaluation_time": 61.65, | |
| "zh": { | |
| "accuracy": 0.47684000000000004, | |
| "accuracy_stderr": 0.013742430643812614, | |
| "f1": 0.4576679486195056, | |
| "f1_stderr": 0.011971035532689783, | |
| "main_score": 0.47684000000000004 | |
| } | |
| } | |
| } |