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": "7d571f92784cd94a019292a1f45445077d0ef634", | |
| "mteb_dataset_name": "MassiveScenarioClassification", | |
| "mteb_version": "1.1.2", | |
| "test": { | |
| "evaluation_time": 10.25, | |
| "zh-CN": { | |
| "accuracy": 0.7842299932750503, | |
| "accuracy_stderr": 0.014996826003562261, | |
| "f1": 0.7810148999942874, | |
| "f1_stderr": 0.013606032311831804, | |
| "main_score": 0.7842299932750503 | |
| } | |
| }, | |
| "validation": { | |
| "evaluation_time": 18.47, | |
| "zh-CN": { | |
| "accuracy": 0.7782587309394983, | |
| "accuracy_stderr": 0.014369073372113914, | |
| "f1": 0.7723778920308015, | |
| "f1_stderr": 0.015256127169547022, | |
| "main_score": 0.7782587309394983 | |
| } | |
| } | |
| } |