Feature Extraction
sentence-transformers
Safetensors
English
bert
mteb
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use qinxianliu/FAE-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use qinxianliu/FAE-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("qinxianliu/FAE-v1") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Downloads last month
- 132
Model tree for qinxianliu/FAE-v1
Evaluation results
- main_score on MTEB ArguAna (default)test set self-reported46.744
- map_at_1 on MTEB ArguAna (default)test set self-reported22.404
- map_at_10 on MTEB ArguAna (default)test set self-reported37.445
- map_at_100 on MTEB ArguAna (default)test set self-reported38.608
- map_at_1000 on MTEB ArguAna (default)test set self-reported38.617
- map_at_20 on MTEB ArguAna (default)test set self-reported38.337
- map_at_3 on MTEB ArguAna (default)test set self-reported32.112
- map_at_5 on MTEB ArguAna (default)test set self-reported34.815