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: 384 Bytes
4ec0bf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"dataset_revision": "46958b007a63fdbf239b7672c25d0bea67b5ea1a",
"mteb_dataset_name": "MultilingualSentiment",
"mteb_version": "1.1.2",
"validation": {
"accuracy": 0.7756333333333334,
"accuracy_stderr": 0.004520201568760209,
"evaluation_time": 26.01,
"f1": 0.7753666660124703,
"f1_stderr": 0.0060536558948603575,
"main_score": 0.7756333333333334
}
} |