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
File size: 681 Bytes
4ec0bf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"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
}
}
} |