Commit
·
044cc3e
1
Parent(s):
d8ae5cf
Add MTEB
Browse files
README.md
CHANGED
@@ -4,6 +4,2397 @@ tags:
|
|
4 |
- sentence-transformers
|
5 |
- feature-extraction
|
6 |
- sentence-similarity
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
7 |
---
|
8 |
|
9 |
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit
|
|
|
4 |
- sentence-transformers
|
5 |
- feature-extraction
|
6 |
- sentence-similarity
|
7 |
+
model-index:
|
8 |
+
- name: SGPT-1.3B-weightedmean-msmarco-specb-bitfit
|
9 |
+
results:
|
10 |
+
- task:
|
11 |
+
type: Classification
|
12 |
+
dataset:
|
13 |
+
type: mteb/amazon_counterfactual
|
14 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
15 |
+
metrics:
|
16 |
+
- type: accuracy
|
17 |
+
value: 65.20895522388061
|
18 |
+
- type: ap
|
19 |
+
value: 29.59212705444778
|
20 |
+
- type: f1
|
21 |
+
value: 59.97099864321921
|
22 |
+
- task:
|
23 |
+
type: Classification
|
24 |
+
dataset:
|
25 |
+
type: mteb/amazon_polarity
|
26 |
+
name: MTEB AmazonPolarityClassification
|
27 |
+
metrics:
|
28 |
+
- type: accuracy
|
29 |
+
value: 73.20565
|
30 |
+
- type: ap
|
31 |
+
value: 67.36680643550963
|
32 |
+
- type: f1
|
33 |
+
value: 72.90420520325125
|
34 |
+
- task:
|
35 |
+
type: Classification
|
36 |
+
dataset:
|
37 |
+
type: mteb/amazon_reviews_multi
|
38 |
+
name: MTEB AmazonReviewsClassification (en)
|
39 |
+
metrics:
|
40 |
+
- type: accuracy
|
41 |
+
value: 34.955999999999996
|
42 |
+
- type: f1
|
43 |
+
value: 34.719324437696955
|
44 |
+
- task:
|
45 |
+
type: Retrieval
|
46 |
+
dataset:
|
47 |
+
type: arguana
|
48 |
+
name: MTEB ArguAna
|
49 |
+
metrics:
|
50 |
+
- type: map_at_1
|
51 |
+
value: 26.101999999999997
|
52 |
+
- type: map_at_10
|
53 |
+
value: 40.958
|
54 |
+
- type: map_at_100
|
55 |
+
value: 42.033
|
56 |
+
- type: map_at_1000
|
57 |
+
value: 42.042
|
58 |
+
- type: map_at_3
|
59 |
+
value: 36.332
|
60 |
+
- type: map_at_5
|
61 |
+
value: 38.608
|
62 |
+
- type: mrr_at_1
|
63 |
+
value: 26.387
|
64 |
+
- type: mrr_at_10
|
65 |
+
value: 41.051
|
66 |
+
- type: mrr_at_100
|
67 |
+
value: 42.118
|
68 |
+
- type: mrr_at_1000
|
69 |
+
value: 42.126999999999995
|
70 |
+
- type: mrr_at_3
|
71 |
+
value: 36.415
|
72 |
+
- type: mrr_at_5
|
73 |
+
value: 38.72
|
74 |
+
- type: ndcg_at_1
|
75 |
+
value: 26.101999999999997
|
76 |
+
- type: ndcg_at_10
|
77 |
+
value: 49.68
|
78 |
+
- type: ndcg_at_100
|
79 |
+
value: 54.257999999999996
|
80 |
+
- type: ndcg_at_1000
|
81 |
+
value: 54.486000000000004
|
82 |
+
- type: ndcg_at_3
|
83 |
+
value: 39.864
|
84 |
+
- type: ndcg_at_5
|
85 |
+
value: 43.980000000000004
|
86 |
+
- type: precision_at_1
|
87 |
+
value: 26.101999999999997
|
88 |
+
- type: precision_at_10
|
89 |
+
value: 7.781000000000001
|
90 |
+
- type: precision_at_100
|
91 |
+
value: 0.979
|
92 |
+
- type: precision_at_1000
|
93 |
+
value: 0.1
|
94 |
+
- type: precision_at_3
|
95 |
+
value: 16.714000000000002
|
96 |
+
- type: precision_at_5
|
97 |
+
value: 12.034
|
98 |
+
- type: recall_at_1
|
99 |
+
value: 26.101999999999997
|
100 |
+
- type: recall_at_10
|
101 |
+
value: 77.809
|
102 |
+
- type: recall_at_100
|
103 |
+
value: 97.866
|
104 |
+
- type: recall_at_1000
|
105 |
+
value: 99.644
|
106 |
+
- type: recall_at_3
|
107 |
+
value: 50.141999999999996
|
108 |
+
- type: recall_at_5
|
109 |
+
value: 60.171
|
110 |
+
- task:
|
111 |
+
type: Clustering
|
112 |
+
dataset:
|
113 |
+
type: mteb/arxiv-clustering-p2p
|
114 |
+
name: MTEB ArxivClusteringP2P
|
115 |
+
metrics:
|
116 |
+
- type: v_measure
|
117 |
+
value: 43.384194916953774
|
118 |
+
- task:
|
119 |
+
type: Clustering
|
120 |
+
dataset:
|
121 |
+
type: mteb/arxiv-clustering-s2s
|
122 |
+
name: MTEB ArxivClusteringS2S
|
123 |
+
metrics:
|
124 |
+
- type: v_measure
|
125 |
+
value: 33.70962633433912
|
126 |
+
- task:
|
127 |
+
type: Reranking
|
128 |
+
dataset:
|
129 |
+
type: mteb/askubuntudupquestions-reranking
|
130 |
+
name: MTEB AskUbuntuDupQuestions
|
131 |
+
metrics:
|
132 |
+
- type: map
|
133 |
+
value: 58.133058996870076
|
134 |
+
- type: mrr
|
135 |
+
value: 72.10922041946972
|
136 |
+
- task:
|
137 |
+
type: STS
|
138 |
+
dataset:
|
139 |
+
type: mteb/biosses-sts
|
140 |
+
name: MTEB BIOSSES
|
141 |
+
metrics:
|
142 |
+
- type: cos_sim_pearson
|
143 |
+
value: 86.62153841660047
|
144 |
+
- type: cos_sim_spearman
|
145 |
+
value: 83.01514456843276
|
146 |
+
- type: euclidean_pearson
|
147 |
+
value: 86.00431518427241
|
148 |
+
- type: euclidean_spearman
|
149 |
+
value: 83.85552516285783
|
150 |
+
- type: manhattan_pearson
|
151 |
+
value: 85.83025803351181
|
152 |
+
- type: manhattan_spearman
|
153 |
+
value: 83.86636878343106
|
154 |
+
- task:
|
155 |
+
type: Classification
|
156 |
+
dataset:
|
157 |
+
type: mteb/banking77
|
158 |
+
name: MTEB Banking77Classification
|
159 |
+
metrics:
|
160 |
+
- type: accuracy
|
161 |
+
value: 82.05844155844156
|
162 |
+
- type: f1
|
163 |
+
value: 82.0185837884764
|
164 |
+
- task:
|
165 |
+
type: Clustering
|
166 |
+
dataset:
|
167 |
+
type: mteb/biorxiv-clustering-p2p
|
168 |
+
name: MTEB BiorxivClusteringP2P
|
169 |
+
metrics:
|
170 |
+
- type: v_measure
|
171 |
+
value: 35.05918333141837
|
172 |
+
- task:
|
173 |
+
type: Clustering
|
174 |
+
dataset:
|
175 |
+
type: mteb/biorxiv-clustering-s2s
|
176 |
+
name: MTEB BiorxivClusteringS2S
|
177 |
+
metrics:
|
178 |
+
- type: v_measure
|
179 |
+
value: 30.71055028830579
|
180 |
+
- task:
|
181 |
+
type: Retrieval
|
182 |
+
dataset:
|
183 |
+
type: BeIR/cqadupstack
|
184 |
+
name: MTEB CQADupstackAndroidRetrieval
|
185 |
+
metrics:
|
186 |
+
- type: map_at_1
|
187 |
+
value: 26.519
|
188 |
+
- type: map_at_10
|
189 |
+
value: 35.634
|
190 |
+
- type: map_at_100
|
191 |
+
value: 36.961
|
192 |
+
- type: map_at_1000
|
193 |
+
value: 37.088
|
194 |
+
- type: map_at_3
|
195 |
+
value: 32.254
|
196 |
+
- type: map_at_5
|
197 |
+
value: 34.22
|
198 |
+
- type: mrr_at_1
|
199 |
+
value: 32.332
|
200 |
+
- type: mrr_at_10
|
201 |
+
value: 41.168
|
202 |
+
- type: mrr_at_100
|
203 |
+
value: 41.977
|
204 |
+
- type: mrr_at_1000
|
205 |
+
value: 42.028999999999996
|
206 |
+
- type: mrr_at_3
|
207 |
+
value: 38.196999999999996
|
208 |
+
- type: mrr_at_5
|
209 |
+
value: 40.036
|
210 |
+
- type: ndcg_at_1
|
211 |
+
value: 32.332
|
212 |
+
- type: ndcg_at_10
|
213 |
+
value: 41.471000000000004
|
214 |
+
- type: ndcg_at_100
|
215 |
+
value: 46.955999999999996
|
216 |
+
- type: ndcg_at_1000
|
217 |
+
value: 49.262
|
218 |
+
- type: ndcg_at_3
|
219 |
+
value: 35.937999999999995
|
220 |
+
- type: ndcg_at_5
|
221 |
+
value: 38.702999999999996
|
222 |
+
- type: precision_at_1
|
223 |
+
value: 32.332
|
224 |
+
- type: precision_at_10
|
225 |
+
value: 7.7829999999999995
|
226 |
+
- type: precision_at_100
|
227 |
+
value: 1.29
|
228 |
+
- type: precision_at_1000
|
229 |
+
value: 0.178
|
230 |
+
- type: precision_at_3
|
231 |
+
value: 16.834
|
232 |
+
- type: precision_at_5
|
233 |
+
value: 12.418
|
234 |
+
- type: recall_at_1
|
235 |
+
value: 26.519
|
236 |
+
- type: recall_at_10
|
237 |
+
value: 53.190000000000005
|
238 |
+
- type: recall_at_100
|
239 |
+
value: 76.56500000000001
|
240 |
+
- type: recall_at_1000
|
241 |
+
value: 91.47800000000001
|
242 |
+
- type: recall_at_3
|
243 |
+
value: 38.034
|
244 |
+
- type: recall_at_5
|
245 |
+
value: 45.245999999999995
|
246 |
+
- task:
|
247 |
+
type: Retrieval
|
248 |
+
dataset:
|
249 |
+
type: BeIR/cqadupstack
|
250 |
+
name: MTEB CQADupstackEnglishRetrieval
|
251 |
+
metrics:
|
252 |
+
- type: map_at_1
|
253 |
+
value: 25.356
|
254 |
+
- type: map_at_10
|
255 |
+
value: 34.596
|
256 |
+
- type: map_at_100
|
257 |
+
value: 35.714
|
258 |
+
- type: map_at_1000
|
259 |
+
value: 35.839999999999996
|
260 |
+
- type: map_at_3
|
261 |
+
value: 32.073
|
262 |
+
- type: map_at_5
|
263 |
+
value: 33.475
|
264 |
+
- type: mrr_at_1
|
265 |
+
value: 31.274
|
266 |
+
- type: mrr_at_10
|
267 |
+
value: 39.592
|
268 |
+
- type: mrr_at_100
|
269 |
+
value: 40.284
|
270 |
+
- type: mrr_at_1000
|
271 |
+
value: 40.339999999999996
|
272 |
+
- type: mrr_at_3
|
273 |
+
value: 37.378
|
274 |
+
- type: mrr_at_5
|
275 |
+
value: 38.658
|
276 |
+
- type: ndcg_at_1
|
277 |
+
value: 31.274
|
278 |
+
- type: ndcg_at_10
|
279 |
+
value: 39.766
|
280 |
+
- type: ndcg_at_100
|
281 |
+
value: 44.028
|
282 |
+
- type: ndcg_at_1000
|
283 |
+
value: 46.445
|
284 |
+
- type: ndcg_at_3
|
285 |
+
value: 35.934
|
286 |
+
- type: ndcg_at_5
|
287 |
+
value: 37.751000000000005
|
288 |
+
- type: precision_at_1
|
289 |
+
value: 31.274
|
290 |
+
- type: precision_at_10
|
291 |
+
value: 7.452
|
292 |
+
- type: precision_at_100
|
293 |
+
value: 1.217
|
294 |
+
- type: precision_at_1000
|
295 |
+
value: 0.16999999999999998
|
296 |
+
- type: precision_at_3
|
297 |
+
value: 17.431
|
298 |
+
- type: precision_at_5
|
299 |
+
value: 12.306000000000001
|
300 |
+
- type: recall_at_1
|
301 |
+
value: 25.356
|
302 |
+
- type: recall_at_10
|
303 |
+
value: 49.344
|
304 |
+
- type: recall_at_100
|
305 |
+
value: 67.497
|
306 |
+
- type: recall_at_1000
|
307 |
+
value: 83.372
|
308 |
+
- type: recall_at_3
|
309 |
+
value: 38.227
|
310 |
+
- type: recall_at_5
|
311 |
+
value: 43.187999999999995
|
312 |
+
- task:
|
313 |
+
type: Retrieval
|
314 |
+
dataset:
|
315 |
+
type: BeIR/cqadupstack
|
316 |
+
name: MTEB CQADupstackGamingRetrieval
|
317 |
+
metrics:
|
318 |
+
- type: map_at_1
|
319 |
+
value: 32.759
|
320 |
+
- type: map_at_10
|
321 |
+
value: 43.937
|
322 |
+
- type: map_at_100
|
323 |
+
value: 45.004
|
324 |
+
- type: map_at_1000
|
325 |
+
value: 45.07
|
326 |
+
- type: map_at_3
|
327 |
+
value: 40.805
|
328 |
+
- type: map_at_5
|
329 |
+
value: 42.497
|
330 |
+
- type: mrr_at_1
|
331 |
+
value: 37.367
|
332 |
+
- type: mrr_at_10
|
333 |
+
value: 47.237
|
334 |
+
- type: mrr_at_100
|
335 |
+
value: 47.973
|
336 |
+
- type: mrr_at_1000
|
337 |
+
value: 48.010999999999996
|
338 |
+
- type: mrr_at_3
|
339 |
+
value: 44.65
|
340 |
+
- type: mrr_at_5
|
341 |
+
value: 46.050999999999995
|
342 |
+
- type: ndcg_at_1
|
343 |
+
value: 37.367
|
344 |
+
- type: ndcg_at_10
|
345 |
+
value: 49.659
|
346 |
+
- type: ndcg_at_100
|
347 |
+
value: 54.069
|
348 |
+
- type: ndcg_at_1000
|
349 |
+
value: 55.552
|
350 |
+
- type: ndcg_at_3
|
351 |
+
value: 44.169000000000004
|
352 |
+
- type: ndcg_at_5
|
353 |
+
value: 46.726
|
354 |
+
- type: precision_at_1
|
355 |
+
value: 37.367
|
356 |
+
- type: precision_at_10
|
357 |
+
value: 8.163
|
358 |
+
- type: precision_at_100
|
359 |
+
value: 1.133
|
360 |
+
- type: precision_at_1000
|
361 |
+
value: 0.131
|
362 |
+
- type: precision_at_3
|
363 |
+
value: 19.707
|
364 |
+
- type: precision_at_5
|
365 |
+
value: 13.718
|
366 |
+
- type: recall_at_1
|
367 |
+
value: 32.759
|
368 |
+
- type: recall_at_10
|
369 |
+
value: 63.341
|
370 |
+
- type: recall_at_100
|
371 |
+
value: 82.502
|
372 |
+
- type: recall_at_1000
|
373 |
+
value: 93.259
|
374 |
+
- type: recall_at_3
|
375 |
+
value: 48.796
|
376 |
+
- type: recall_at_5
|
377 |
+
value: 54.921
|
378 |
+
- task:
|
379 |
+
type: Retrieval
|
380 |
+
dataset:
|
381 |
+
type: BeIR/cqadupstack
|
382 |
+
name: MTEB CQADupstackGisRetrieval
|
383 |
+
metrics:
|
384 |
+
- type: map_at_1
|
385 |
+
value: 18.962
|
386 |
+
- type: map_at_10
|
387 |
+
value: 25.863000000000003
|
388 |
+
- type: map_at_100
|
389 |
+
value: 26.817999999999998
|
390 |
+
- type: map_at_1000
|
391 |
+
value: 26.918
|
392 |
+
- type: map_at_3
|
393 |
+
value: 23.043
|
394 |
+
- type: map_at_5
|
395 |
+
value: 24.599
|
396 |
+
- type: mrr_at_1
|
397 |
+
value: 20.452
|
398 |
+
- type: mrr_at_10
|
399 |
+
value: 27.301
|
400 |
+
- type: mrr_at_100
|
401 |
+
value: 28.233000000000004
|
402 |
+
- type: mrr_at_1000
|
403 |
+
value: 28.310000000000002
|
404 |
+
- type: mrr_at_3
|
405 |
+
value: 24.539
|
406 |
+
- type: mrr_at_5
|
407 |
+
value: 26.108999999999998
|
408 |
+
- type: ndcg_at_1
|
409 |
+
value: 20.452
|
410 |
+
- type: ndcg_at_10
|
411 |
+
value: 30.354999999999997
|
412 |
+
- type: ndcg_at_100
|
413 |
+
value: 35.336
|
414 |
+
- type: ndcg_at_1000
|
415 |
+
value: 37.927
|
416 |
+
- type: ndcg_at_3
|
417 |
+
value: 24.705
|
418 |
+
- type: ndcg_at_5
|
419 |
+
value: 27.42
|
420 |
+
- type: precision_at_1
|
421 |
+
value: 20.452
|
422 |
+
- type: precision_at_10
|
423 |
+
value: 4.949
|
424 |
+
- type: precision_at_100
|
425 |
+
value: 0.7799999999999999
|
426 |
+
- type: precision_at_1000
|
427 |
+
value: 0.104
|
428 |
+
- type: precision_at_3
|
429 |
+
value: 10.358
|
430 |
+
- type: precision_at_5
|
431 |
+
value: 7.774
|
432 |
+
- type: recall_at_1
|
433 |
+
value: 18.962
|
434 |
+
- type: recall_at_10
|
435 |
+
value: 43.056
|
436 |
+
- type: recall_at_100
|
437 |
+
value: 66.27300000000001
|
438 |
+
- type: recall_at_1000
|
439 |
+
value: 85.96000000000001
|
440 |
+
- type: recall_at_3
|
441 |
+
value: 27.776
|
442 |
+
- type: recall_at_5
|
443 |
+
value: 34.287
|
444 |
+
- task:
|
445 |
+
type: Retrieval
|
446 |
+
dataset:
|
447 |
+
type: BeIR/cqadupstack
|
448 |
+
name: MTEB CQADupstackMathematicaRetrieval
|
449 |
+
metrics:
|
450 |
+
- type: map_at_1
|
451 |
+
value: 11.24
|
452 |
+
- type: map_at_10
|
453 |
+
value: 18.503
|
454 |
+
- type: map_at_100
|
455 |
+
value: 19.553
|
456 |
+
- type: map_at_1000
|
457 |
+
value: 19.689999999999998
|
458 |
+
- type: map_at_3
|
459 |
+
value: 16.150000000000002
|
460 |
+
- type: map_at_5
|
461 |
+
value: 17.254
|
462 |
+
- type: mrr_at_1
|
463 |
+
value: 13.806
|
464 |
+
- type: mrr_at_10
|
465 |
+
value: 21.939
|
466 |
+
- type: mrr_at_100
|
467 |
+
value: 22.827
|
468 |
+
- type: mrr_at_1000
|
469 |
+
value: 22.911
|
470 |
+
- type: mrr_at_3
|
471 |
+
value: 19.32
|
472 |
+
- type: mrr_at_5
|
473 |
+
value: 20.558
|
474 |
+
- type: ndcg_at_1
|
475 |
+
value: 13.806
|
476 |
+
- type: ndcg_at_10
|
477 |
+
value: 23.383000000000003
|
478 |
+
- type: ndcg_at_100
|
479 |
+
value: 28.834
|
480 |
+
- type: ndcg_at_1000
|
481 |
+
value: 32.175
|
482 |
+
- type: ndcg_at_3
|
483 |
+
value: 18.651999999999997
|
484 |
+
- type: ndcg_at_5
|
485 |
+
value: 20.505000000000003
|
486 |
+
- type: precision_at_1
|
487 |
+
value: 13.806
|
488 |
+
- type: precision_at_10
|
489 |
+
value: 4.714
|
490 |
+
- type: precision_at_100
|
491 |
+
value: 0.864
|
492 |
+
- type: precision_at_1000
|
493 |
+
value: 0.13
|
494 |
+
- type: precision_at_3
|
495 |
+
value: 9.328
|
496 |
+
- type: precision_at_5
|
497 |
+
value: 6.841
|
498 |
+
- type: recall_at_1
|
499 |
+
value: 11.24
|
500 |
+
- type: recall_at_10
|
501 |
+
value: 34.854
|
502 |
+
- type: recall_at_100
|
503 |
+
value: 59.50299999999999
|
504 |
+
- type: recall_at_1000
|
505 |
+
value: 83.25
|
506 |
+
- type: recall_at_3
|
507 |
+
value: 22.02
|
508 |
+
- type: recall_at_5
|
509 |
+
value: 26.715
|
510 |
+
- task:
|
511 |
+
type: Retrieval
|
512 |
+
dataset:
|
513 |
+
type: BeIR/cqadupstack
|
514 |
+
name: MTEB CQADupstackPhysicsRetrieval
|
515 |
+
metrics:
|
516 |
+
- type: map_at_1
|
517 |
+
value: 23.012
|
518 |
+
- type: map_at_10
|
519 |
+
value: 33.048
|
520 |
+
- type: map_at_100
|
521 |
+
value: 34.371
|
522 |
+
- type: map_at_1000
|
523 |
+
value: 34.489
|
524 |
+
- type: map_at_3
|
525 |
+
value: 29.942999999999998
|
526 |
+
- type: map_at_5
|
527 |
+
value: 31.602000000000004
|
528 |
+
- type: mrr_at_1
|
529 |
+
value: 28.104000000000003
|
530 |
+
- type: mrr_at_10
|
531 |
+
value: 37.99
|
532 |
+
- type: mrr_at_100
|
533 |
+
value: 38.836
|
534 |
+
- type: mrr_at_1000
|
535 |
+
value: 38.891
|
536 |
+
- type: mrr_at_3
|
537 |
+
value: 35.226
|
538 |
+
- type: mrr_at_5
|
539 |
+
value: 36.693999999999996
|
540 |
+
- type: ndcg_at_1
|
541 |
+
value: 28.104000000000003
|
542 |
+
- type: ndcg_at_10
|
543 |
+
value: 39.037
|
544 |
+
- type: ndcg_at_100
|
545 |
+
value: 44.643
|
546 |
+
- type: ndcg_at_1000
|
547 |
+
value: 46.939
|
548 |
+
- type: ndcg_at_3
|
549 |
+
value: 33.784
|
550 |
+
- type: ndcg_at_5
|
551 |
+
value: 36.126000000000005
|
552 |
+
- type: precision_at_1
|
553 |
+
value: 28.104000000000003
|
554 |
+
- type: precision_at_10
|
555 |
+
value: 7.2669999999999995
|
556 |
+
- type: precision_at_100
|
557 |
+
value: 1.193
|
558 |
+
- type: precision_at_1000
|
559 |
+
value: 0.159
|
560 |
+
- type: precision_at_3
|
561 |
+
value: 16.298000000000002
|
562 |
+
- type: precision_at_5
|
563 |
+
value: 11.684
|
564 |
+
- type: recall_at_1
|
565 |
+
value: 23.012
|
566 |
+
- type: recall_at_10
|
567 |
+
value: 52.054
|
568 |
+
- type: recall_at_100
|
569 |
+
value: 75.622
|
570 |
+
- type: recall_at_1000
|
571 |
+
value: 90.675
|
572 |
+
- type: recall_at_3
|
573 |
+
value: 37.282
|
574 |
+
- type: recall_at_5
|
575 |
+
value: 43.307
|
576 |
+
- task:
|
577 |
+
type: Retrieval
|
578 |
+
dataset:
|
579 |
+
type: BeIR/cqadupstack
|
580 |
+
name: MTEB CQADupstackProgrammersRetrieval
|
581 |
+
metrics:
|
582 |
+
- type: map_at_1
|
583 |
+
value: 21.624
|
584 |
+
- type: map_at_10
|
585 |
+
value: 30.209999999999997
|
586 |
+
- type: map_at_100
|
587 |
+
value: 31.52
|
588 |
+
- type: map_at_1000
|
589 |
+
value: 31.625999999999998
|
590 |
+
- type: map_at_3
|
591 |
+
value: 26.951000000000004
|
592 |
+
- type: map_at_5
|
593 |
+
value: 28.938999999999997
|
594 |
+
- type: mrr_at_1
|
595 |
+
value: 26.941
|
596 |
+
- type: mrr_at_10
|
597 |
+
value: 35.13
|
598 |
+
- type: mrr_at_100
|
599 |
+
value: 36.15
|
600 |
+
- type: mrr_at_1000
|
601 |
+
value: 36.204
|
602 |
+
- type: mrr_at_3
|
603 |
+
value: 32.42
|
604 |
+
- type: mrr_at_5
|
605 |
+
value: 34.155
|
606 |
+
- type: ndcg_at_1
|
607 |
+
value: 26.941
|
608 |
+
- type: ndcg_at_10
|
609 |
+
value: 35.726
|
610 |
+
- type: ndcg_at_100
|
611 |
+
value: 41.725
|
612 |
+
- type: ndcg_at_1000
|
613 |
+
value: 44.105
|
614 |
+
- type: ndcg_at_3
|
615 |
+
value: 30.184
|
616 |
+
- type: ndcg_at_5
|
617 |
+
value: 33.176
|
618 |
+
- type: precision_at_1
|
619 |
+
value: 26.941
|
620 |
+
- type: precision_at_10
|
621 |
+
value: 6.654999999999999
|
622 |
+
- type: precision_at_100
|
623 |
+
value: 1.1520000000000001
|
624 |
+
- type: precision_at_1000
|
625 |
+
value: 0.152
|
626 |
+
- type: precision_at_3
|
627 |
+
value: 14.346
|
628 |
+
- type: precision_at_5
|
629 |
+
value: 10.868
|
630 |
+
- type: recall_at_1
|
631 |
+
value: 21.624
|
632 |
+
- type: recall_at_10
|
633 |
+
value: 47.359
|
634 |
+
- type: recall_at_100
|
635 |
+
value: 73.436
|
636 |
+
- type: recall_at_1000
|
637 |
+
value: 89.988
|
638 |
+
- type: recall_at_3
|
639 |
+
value: 32.34
|
640 |
+
- type: recall_at_5
|
641 |
+
value: 39.856
|
642 |
+
- task:
|
643 |
+
type: Retrieval
|
644 |
+
dataset:
|
645 |
+
type: BeIR/cqadupstack
|
646 |
+
name: MTEB CQADupstackRetrieval
|
647 |
+
metrics:
|
648 |
+
- type: map_at_1
|
649 |
+
value: 20.67566666666667
|
650 |
+
- type: map_at_10
|
651 |
+
value: 28.479333333333333
|
652 |
+
- type: map_at_100
|
653 |
+
value: 29.612249999999996
|
654 |
+
- type: map_at_1000
|
655 |
+
value: 29.731166666666663
|
656 |
+
- type: map_at_3
|
657 |
+
value: 25.884
|
658 |
+
- type: map_at_5
|
659 |
+
value: 27.298916666666667
|
660 |
+
- type: mrr_at_1
|
661 |
+
value: 24.402583333333332
|
662 |
+
- type: mrr_at_10
|
663 |
+
value: 32.07041666666667
|
664 |
+
- type: mrr_at_100
|
665 |
+
value: 32.95841666666667
|
666 |
+
- type: mrr_at_1000
|
667 |
+
value: 33.025416666666665
|
668 |
+
- type: mrr_at_3
|
669 |
+
value: 29.677749999999996
|
670 |
+
- type: mrr_at_5
|
671 |
+
value: 31.02391666666667
|
672 |
+
- type: ndcg_at_1
|
673 |
+
value: 24.402583333333332
|
674 |
+
- type: ndcg_at_10
|
675 |
+
value: 33.326166666666666
|
676 |
+
- type: ndcg_at_100
|
677 |
+
value: 38.51566666666667
|
678 |
+
- type: ndcg_at_1000
|
679 |
+
value: 41.13791666666667
|
680 |
+
- type: ndcg_at_3
|
681 |
+
value: 28.687749999999994
|
682 |
+
- type: ndcg_at_5
|
683 |
+
value: 30.84766666666667
|
684 |
+
- type: precision_at_1
|
685 |
+
value: 24.402583333333332
|
686 |
+
- type: precision_at_10
|
687 |
+
value: 5.943749999999999
|
688 |
+
- type: precision_at_100
|
689 |
+
value: 1.0098333333333334
|
690 |
+
- type: precision_at_1000
|
691 |
+
value: 0.14183333333333334
|
692 |
+
- type: precision_at_3
|
693 |
+
value: 13.211500000000001
|
694 |
+
- type: precision_at_5
|
695 |
+
value: 9.548416666666668
|
696 |
+
- type: recall_at_1
|
697 |
+
value: 20.67566666666667
|
698 |
+
- type: recall_at_10
|
699 |
+
value: 44.245583333333336
|
700 |
+
- type: recall_at_100
|
701 |
+
value: 67.31116666666667
|
702 |
+
- type: recall_at_1000
|
703 |
+
value: 85.87841666666665
|
704 |
+
- type: recall_at_3
|
705 |
+
value: 31.49258333333333
|
706 |
+
- type: recall_at_5
|
707 |
+
value: 36.93241666666667
|
708 |
+
- task:
|
709 |
+
type: Retrieval
|
710 |
+
dataset:
|
711 |
+
type: BeIR/cqadupstack
|
712 |
+
name: MTEB CQADupstackStatsRetrieval
|
713 |
+
metrics:
|
714 |
+
- type: map_at_1
|
715 |
+
value: 18.34
|
716 |
+
- type: map_at_10
|
717 |
+
value: 23.988
|
718 |
+
- type: map_at_100
|
719 |
+
value: 24.895
|
720 |
+
- type: map_at_1000
|
721 |
+
value: 24.992
|
722 |
+
- type: map_at_3
|
723 |
+
value: 21.831
|
724 |
+
- type: map_at_5
|
725 |
+
value: 23.0
|
726 |
+
- type: mrr_at_1
|
727 |
+
value: 20.399
|
728 |
+
- type: mrr_at_10
|
729 |
+
value: 26.186
|
730 |
+
- type: mrr_at_100
|
731 |
+
value: 27.017999999999997
|
732 |
+
- type: mrr_at_1000
|
733 |
+
value: 27.090999999999998
|
734 |
+
- type: mrr_at_3
|
735 |
+
value: 24.08
|
736 |
+
- type: mrr_at_5
|
737 |
+
value: 25.230000000000004
|
738 |
+
- type: ndcg_at_1
|
739 |
+
value: 20.399
|
740 |
+
- type: ndcg_at_10
|
741 |
+
value: 27.799000000000003
|
742 |
+
- type: ndcg_at_100
|
743 |
+
value: 32.579
|
744 |
+
- type: ndcg_at_1000
|
745 |
+
value: 35.209
|
746 |
+
- type: ndcg_at_3
|
747 |
+
value: 23.684
|
748 |
+
- type: ndcg_at_5
|
749 |
+
value: 25.521
|
750 |
+
- type: precision_at_1
|
751 |
+
value: 20.399
|
752 |
+
- type: precision_at_10
|
753 |
+
value: 4.585999999999999
|
754 |
+
- type: precision_at_100
|
755 |
+
value: 0.755
|
756 |
+
- type: precision_at_1000
|
757 |
+
value: 0.105
|
758 |
+
- type: precision_at_3
|
759 |
+
value: 10.276
|
760 |
+
- type: precision_at_5
|
761 |
+
value: 7.362
|
762 |
+
- type: recall_at_1
|
763 |
+
value: 18.34
|
764 |
+
- type: recall_at_10
|
765 |
+
value: 37.456
|
766 |
+
- type: recall_at_100
|
767 |
+
value: 59.86
|
768 |
+
- type: recall_at_1000
|
769 |
+
value: 79.703
|
770 |
+
- type: recall_at_3
|
771 |
+
value: 26.163999999999998
|
772 |
+
- type: recall_at_5
|
773 |
+
value: 30.652
|
774 |
+
- task:
|
775 |
+
type: Retrieval
|
776 |
+
dataset:
|
777 |
+
type: BeIR/cqadupstack
|
778 |
+
name: MTEB CQADupstackTexRetrieval
|
779 |
+
metrics:
|
780 |
+
- type: map_at_1
|
781 |
+
value: 12.327
|
782 |
+
- type: map_at_10
|
783 |
+
value: 17.572
|
784 |
+
- type: map_at_100
|
785 |
+
value: 18.534
|
786 |
+
- type: map_at_1000
|
787 |
+
value: 18.653
|
788 |
+
- type: map_at_3
|
789 |
+
value: 15.703
|
790 |
+
- type: map_at_5
|
791 |
+
value: 16.752
|
792 |
+
- type: mrr_at_1
|
793 |
+
value: 15.038000000000002
|
794 |
+
- type: mrr_at_10
|
795 |
+
value: 20.726
|
796 |
+
- type: mrr_at_100
|
797 |
+
value: 21.61
|
798 |
+
- type: mrr_at_1000
|
799 |
+
value: 21.695
|
800 |
+
- type: mrr_at_3
|
801 |
+
value: 18.829
|
802 |
+
- type: mrr_at_5
|
803 |
+
value: 19.885
|
804 |
+
- type: ndcg_at_1
|
805 |
+
value: 15.038000000000002
|
806 |
+
- type: ndcg_at_10
|
807 |
+
value: 21.241
|
808 |
+
- type: ndcg_at_100
|
809 |
+
value: 26.179000000000002
|
810 |
+
- type: ndcg_at_1000
|
811 |
+
value: 29.316
|
812 |
+
- type: ndcg_at_3
|
813 |
+
value: 17.762
|
814 |
+
- type: ndcg_at_5
|
815 |
+
value: 19.413
|
816 |
+
- type: precision_at_1
|
817 |
+
value: 15.038000000000002
|
818 |
+
- type: precision_at_10
|
819 |
+
value: 3.8920000000000003
|
820 |
+
- type: precision_at_100
|
821 |
+
value: 0.75
|
822 |
+
- type: precision_at_1000
|
823 |
+
value: 0.11800000000000001
|
824 |
+
- type: precision_at_3
|
825 |
+
value: 8.351
|
826 |
+
- type: precision_at_5
|
827 |
+
value: 6.187
|
828 |
+
- type: recall_at_1
|
829 |
+
value: 12.327
|
830 |
+
- type: recall_at_10
|
831 |
+
value: 29.342000000000002
|
832 |
+
- type: recall_at_100
|
833 |
+
value: 51.854
|
834 |
+
- type: recall_at_1000
|
835 |
+
value: 74.648
|
836 |
+
- type: recall_at_3
|
837 |
+
value: 19.596
|
838 |
+
- type: recall_at_5
|
839 |
+
value: 23.899
|
840 |
+
- task:
|
841 |
+
type: Retrieval
|
842 |
+
dataset:
|
843 |
+
type: BeIR/cqadupstack
|
844 |
+
name: MTEB CQADupstackUnixRetrieval
|
845 |
+
metrics:
|
846 |
+
- type: map_at_1
|
847 |
+
value: 20.594
|
848 |
+
- type: map_at_10
|
849 |
+
value: 27.878999999999998
|
850 |
+
- type: map_at_100
|
851 |
+
value: 28.926000000000002
|
852 |
+
- type: map_at_1000
|
853 |
+
value: 29.041
|
854 |
+
- type: map_at_3
|
855 |
+
value: 25.668999999999997
|
856 |
+
- type: map_at_5
|
857 |
+
value: 26.773999999999997
|
858 |
+
- type: mrr_at_1
|
859 |
+
value: 23.694000000000003
|
860 |
+
- type: mrr_at_10
|
861 |
+
value: 31.335
|
862 |
+
- type: mrr_at_100
|
863 |
+
value: 32.218
|
864 |
+
- type: mrr_at_1000
|
865 |
+
value: 32.298
|
866 |
+
- type: mrr_at_3
|
867 |
+
value: 29.26
|
868 |
+
- type: mrr_at_5
|
869 |
+
value: 30.328
|
870 |
+
- type: ndcg_at_1
|
871 |
+
value: 23.694000000000003
|
872 |
+
- type: ndcg_at_10
|
873 |
+
value: 32.456
|
874 |
+
- type: ndcg_at_100
|
875 |
+
value: 37.667
|
876 |
+
- type: ndcg_at_1000
|
877 |
+
value: 40.571
|
878 |
+
- type: ndcg_at_3
|
879 |
+
value: 28.283
|
880 |
+
- type: ndcg_at_5
|
881 |
+
value: 29.986
|
882 |
+
- type: precision_at_1
|
883 |
+
value: 23.694000000000003
|
884 |
+
- type: precision_at_10
|
885 |
+
value: 5.448
|
886 |
+
- type: precision_at_100
|
887 |
+
value: 0.9119999999999999
|
888 |
+
- type: precision_at_1000
|
889 |
+
value: 0.127
|
890 |
+
- type: precision_at_3
|
891 |
+
value: 12.717999999999998
|
892 |
+
- type: precision_at_5
|
893 |
+
value: 8.843
|
894 |
+
- type: recall_at_1
|
895 |
+
value: 20.594
|
896 |
+
- type: recall_at_10
|
897 |
+
value: 43.004999999999995
|
898 |
+
- type: recall_at_100
|
899 |
+
value: 66.228
|
900 |
+
- type: recall_at_1000
|
901 |
+
value: 87.17099999999999
|
902 |
+
- type: recall_at_3
|
903 |
+
value: 31.554
|
904 |
+
- type: recall_at_5
|
905 |
+
value: 35.838
|
906 |
+
- task:
|
907 |
+
type: Retrieval
|
908 |
+
dataset:
|
909 |
+
type: BeIR/cqadupstack
|
910 |
+
name: MTEB CQADupstackWebmastersRetrieval
|
911 |
+
metrics:
|
912 |
+
- type: map_at_1
|
913 |
+
value: 20.855999999999998
|
914 |
+
- type: map_at_10
|
915 |
+
value: 28.372000000000003
|
916 |
+
- type: map_at_100
|
917 |
+
value: 29.87
|
918 |
+
- type: map_at_1000
|
919 |
+
value: 30.075000000000003
|
920 |
+
- type: map_at_3
|
921 |
+
value: 26.054
|
922 |
+
- type: map_at_5
|
923 |
+
value: 27.128999999999998
|
924 |
+
- type: mrr_at_1
|
925 |
+
value: 25.494
|
926 |
+
- type: mrr_at_10
|
927 |
+
value: 32.735
|
928 |
+
- type: mrr_at_100
|
929 |
+
value: 33.794000000000004
|
930 |
+
- type: mrr_at_1000
|
931 |
+
value: 33.85
|
932 |
+
- type: mrr_at_3
|
933 |
+
value: 30.731
|
934 |
+
- type: mrr_at_5
|
935 |
+
value: 31.897
|
936 |
+
- type: ndcg_at_1
|
937 |
+
value: 25.494
|
938 |
+
- type: ndcg_at_10
|
939 |
+
value: 33.385
|
940 |
+
- type: ndcg_at_100
|
941 |
+
value: 39.436
|
942 |
+
- type: ndcg_at_1000
|
943 |
+
value: 42.313
|
944 |
+
- type: ndcg_at_3
|
945 |
+
value: 29.612
|
946 |
+
- type: ndcg_at_5
|
947 |
+
value: 31.186999999999998
|
948 |
+
- type: precision_at_1
|
949 |
+
value: 25.494
|
950 |
+
- type: precision_at_10
|
951 |
+
value: 6.422999999999999
|
952 |
+
- type: precision_at_100
|
953 |
+
value: 1.383
|
954 |
+
- type: precision_at_1000
|
955 |
+
value: 0.22399999999999998
|
956 |
+
- type: precision_at_3
|
957 |
+
value: 13.834
|
958 |
+
- type: precision_at_5
|
959 |
+
value: 10.0
|
960 |
+
- type: recall_at_1
|
961 |
+
value: 20.855999999999998
|
962 |
+
- type: recall_at_10
|
963 |
+
value: 42.678
|
964 |
+
- type: recall_at_100
|
965 |
+
value: 70.224
|
966 |
+
- type: recall_at_1000
|
967 |
+
value: 89.369
|
968 |
+
- type: recall_at_3
|
969 |
+
value: 31.957
|
970 |
+
- type: recall_at_5
|
971 |
+
value: 36.026
|
972 |
+
- task:
|
973 |
+
type: Retrieval
|
974 |
+
dataset:
|
975 |
+
type: BeIR/cqadupstack
|
976 |
+
name: MTEB CQADupstackWordpressRetrieval
|
977 |
+
metrics:
|
978 |
+
- type: map_at_1
|
979 |
+
value: 16.519000000000002
|
980 |
+
- type: map_at_10
|
981 |
+
value: 22.15
|
982 |
+
- type: map_at_100
|
983 |
+
value: 23.180999999999997
|
984 |
+
- type: map_at_1000
|
985 |
+
value: 23.291999999999998
|
986 |
+
- type: map_at_3
|
987 |
+
value: 20.132
|
988 |
+
- type: map_at_5
|
989 |
+
value: 21.346
|
990 |
+
- type: mrr_at_1
|
991 |
+
value: 17.93
|
992 |
+
- type: mrr_at_10
|
993 |
+
value: 23.506
|
994 |
+
- type: mrr_at_100
|
995 |
+
value: 24.581
|
996 |
+
- type: mrr_at_1000
|
997 |
+
value: 24.675
|
998 |
+
- type: mrr_at_3
|
999 |
+
value: 21.503
|
1000 |
+
- type: mrr_at_5
|
1001 |
+
value: 22.686
|
1002 |
+
- type: ndcg_at_1
|
1003 |
+
value: 17.93
|
1004 |
+
- type: ndcg_at_10
|
1005 |
+
value: 25.636
|
1006 |
+
- type: ndcg_at_100
|
1007 |
+
value: 30.736
|
1008 |
+
- type: ndcg_at_1000
|
1009 |
+
value: 33.841
|
1010 |
+
- type: ndcg_at_3
|
1011 |
+
value: 21.546000000000003
|
1012 |
+
- type: ndcg_at_5
|
1013 |
+
value: 23.658
|
1014 |
+
- type: precision_at_1
|
1015 |
+
value: 17.93
|
1016 |
+
- type: precision_at_10
|
1017 |
+
value: 3.993
|
1018 |
+
- type: precision_at_100
|
1019 |
+
value: 0.6890000000000001
|
1020 |
+
- type: precision_at_1000
|
1021 |
+
value: 0.104
|
1022 |
+
- type: precision_at_3
|
1023 |
+
value: 9.057
|
1024 |
+
- type: precision_at_5
|
1025 |
+
value: 6.58
|
1026 |
+
- type: recall_at_1
|
1027 |
+
value: 16.519000000000002
|
1028 |
+
- type: recall_at_10
|
1029 |
+
value: 35.268
|
1030 |
+
- type: recall_at_100
|
1031 |
+
value: 58.17
|
1032 |
+
- type: recall_at_1000
|
1033 |
+
value: 81.66799999999999
|
1034 |
+
- type: recall_at_3
|
1035 |
+
value: 24.165
|
1036 |
+
- type: recall_at_5
|
1037 |
+
value: 29.254
|
1038 |
+
- task:
|
1039 |
+
type: Retrieval
|
1040 |
+
dataset:
|
1041 |
+
type: climate-fever
|
1042 |
+
name: MTEB ClimateFEVER
|
1043 |
+
metrics:
|
1044 |
+
- type: map_at_1
|
1045 |
+
value: 10.363
|
1046 |
+
- type: map_at_10
|
1047 |
+
value: 18.301000000000002
|
1048 |
+
- type: map_at_100
|
1049 |
+
value: 20.019000000000002
|
1050 |
+
- type: map_at_1000
|
1051 |
+
value: 20.207
|
1052 |
+
- type: map_at_3
|
1053 |
+
value: 14.877
|
1054 |
+
- type: map_at_5
|
1055 |
+
value: 16.544
|
1056 |
+
- type: mrr_at_1
|
1057 |
+
value: 22.866
|
1058 |
+
- type: mrr_at_10
|
1059 |
+
value: 34.935
|
1060 |
+
- type: mrr_at_100
|
1061 |
+
value: 35.802
|
1062 |
+
- type: mrr_at_1000
|
1063 |
+
value: 35.839999999999996
|
1064 |
+
- type: mrr_at_3
|
1065 |
+
value: 30.965999999999998
|
1066 |
+
- type: mrr_at_5
|
1067 |
+
value: 33.204
|
1068 |
+
- type: ndcg_at_1
|
1069 |
+
value: 22.866
|
1070 |
+
- type: ndcg_at_10
|
1071 |
+
value: 26.595000000000002
|
1072 |
+
- type: ndcg_at_100
|
1073 |
+
value: 33.513999999999996
|
1074 |
+
- type: ndcg_at_1000
|
1075 |
+
value: 36.872
|
1076 |
+
- type: ndcg_at_3
|
1077 |
+
value: 20.666999999999998
|
1078 |
+
- type: ndcg_at_5
|
1079 |
+
value: 22.728
|
1080 |
+
- type: precision_at_1
|
1081 |
+
value: 22.866
|
1082 |
+
- type: precision_at_10
|
1083 |
+
value: 8.632
|
1084 |
+
- type: precision_at_100
|
1085 |
+
value: 1.6119999999999999
|
1086 |
+
- type: precision_at_1000
|
1087 |
+
value: 0.22399999999999998
|
1088 |
+
- type: precision_at_3
|
1089 |
+
value: 15.504999999999999
|
1090 |
+
- type: precision_at_5
|
1091 |
+
value: 12.404
|
1092 |
+
- type: recall_at_1
|
1093 |
+
value: 10.363
|
1094 |
+
- type: recall_at_10
|
1095 |
+
value: 33.494
|
1096 |
+
- type: recall_at_100
|
1097 |
+
value: 57.593
|
1098 |
+
- type: recall_at_1000
|
1099 |
+
value: 76.342
|
1100 |
+
- type: recall_at_3
|
1101 |
+
value: 19.157
|
1102 |
+
- type: recall_at_5
|
1103 |
+
value: 24.637999999999998
|
1104 |
+
- task:
|
1105 |
+
type: Retrieval
|
1106 |
+
dataset:
|
1107 |
+
type: dbpedia-entity
|
1108 |
+
name: MTEB DBPedia
|
1109 |
+
metrics:
|
1110 |
+
- type: map_at_1
|
1111 |
+
value: 7.436
|
1112 |
+
- type: map_at_10
|
1113 |
+
value: 14.760000000000002
|
1114 |
+
- type: map_at_100
|
1115 |
+
value: 19.206
|
1116 |
+
- type: map_at_1000
|
1117 |
+
value: 20.267
|
1118 |
+
- type: map_at_3
|
1119 |
+
value: 10.894
|
1120 |
+
- type: map_at_5
|
1121 |
+
value: 12.828999999999999
|
1122 |
+
- type: mrr_at_1
|
1123 |
+
value: 54.25
|
1124 |
+
- type: mrr_at_10
|
1125 |
+
value: 63.769
|
1126 |
+
- type: mrr_at_100
|
1127 |
+
value: 64.193
|
1128 |
+
- type: mrr_at_1000
|
1129 |
+
value: 64.211
|
1130 |
+
- type: mrr_at_3
|
1131 |
+
value: 61.458
|
1132 |
+
- type: mrr_at_5
|
1133 |
+
value: 63.096
|
1134 |
+
- type: ndcg_at_1
|
1135 |
+
value: 42.875
|
1136 |
+
- type: ndcg_at_10
|
1137 |
+
value: 31.507
|
1138 |
+
- type: ndcg_at_100
|
1139 |
+
value: 34.559
|
1140 |
+
- type: ndcg_at_1000
|
1141 |
+
value: 41.246
|
1142 |
+
- type: ndcg_at_3
|
1143 |
+
value: 35.058
|
1144 |
+
- type: ndcg_at_5
|
1145 |
+
value: 33.396
|
1146 |
+
- type: precision_at_1
|
1147 |
+
value: 54.25
|
1148 |
+
- type: precision_at_10
|
1149 |
+
value: 24.45
|
1150 |
+
- type: precision_at_100
|
1151 |
+
value: 7.383000000000001
|
1152 |
+
- type: precision_at_1000
|
1153 |
+
value: 1.582
|
1154 |
+
- type: precision_at_3
|
1155 |
+
value: 38.083
|
1156 |
+
- type: precision_at_5
|
1157 |
+
value: 32.6
|
1158 |
+
- type: recall_at_1
|
1159 |
+
value: 7.436
|
1160 |
+
- type: recall_at_10
|
1161 |
+
value: 19.862
|
1162 |
+
- type: recall_at_100
|
1163 |
+
value: 38.981
|
1164 |
+
- type: recall_at_1000
|
1165 |
+
value: 61.038000000000004
|
1166 |
+
- type: recall_at_3
|
1167 |
+
value: 11.949
|
1168 |
+
- type: recall_at_5
|
1169 |
+
value: 15.562000000000001
|
1170 |
+
- task:
|
1171 |
+
type: Classification
|
1172 |
+
dataset:
|
1173 |
+
type: mteb/emotion
|
1174 |
+
name: MTEB EmotionClassification
|
1175 |
+
metrics:
|
1176 |
+
- type: accuracy
|
1177 |
+
value: 46.39
|
1178 |
+
- type: f1
|
1179 |
+
value: 42.26424885856703
|
1180 |
+
- task:
|
1181 |
+
type: Retrieval
|
1182 |
+
dataset:
|
1183 |
+
type: fever
|
1184 |
+
name: MTEB FEVER
|
1185 |
+
metrics:
|
1186 |
+
- type: map_at_1
|
1187 |
+
value: 50.916
|
1188 |
+
- type: map_at_10
|
1189 |
+
value: 62.258
|
1190 |
+
- type: map_at_100
|
1191 |
+
value: 62.741
|
1192 |
+
- type: map_at_1000
|
1193 |
+
value: 62.763000000000005
|
1194 |
+
- type: map_at_3
|
1195 |
+
value: 60.01800000000001
|
1196 |
+
- type: map_at_5
|
1197 |
+
value: 61.419999999999995
|
1198 |
+
- type: mrr_at_1
|
1199 |
+
value: 54.964999999999996
|
1200 |
+
- type: mrr_at_10
|
1201 |
+
value: 66.554
|
1202 |
+
- type: mrr_at_100
|
1203 |
+
value: 66.96600000000001
|
1204 |
+
- type: mrr_at_1000
|
1205 |
+
value: 66.97800000000001
|
1206 |
+
- type: mrr_at_3
|
1207 |
+
value: 64.414
|
1208 |
+
- type: mrr_at_5
|
1209 |
+
value: 65.77
|
1210 |
+
- type: ndcg_at_1
|
1211 |
+
value: 54.964999999999996
|
1212 |
+
- type: ndcg_at_10
|
1213 |
+
value: 68.12
|
1214 |
+
- type: ndcg_at_100
|
1215 |
+
value: 70.282
|
1216 |
+
- type: ndcg_at_1000
|
1217 |
+
value: 70.788
|
1218 |
+
- type: ndcg_at_3
|
1219 |
+
value: 63.861999999999995
|
1220 |
+
- type: ndcg_at_5
|
1221 |
+
value: 66.216
|
1222 |
+
- type: precision_at_1
|
1223 |
+
value: 54.964999999999996
|
1224 |
+
- type: precision_at_10
|
1225 |
+
value: 8.998000000000001
|
1226 |
+
- type: precision_at_100
|
1227 |
+
value: 1.016
|
1228 |
+
- type: precision_at_1000
|
1229 |
+
value: 0.107
|
1230 |
+
- type: precision_at_3
|
1231 |
+
value: 25.618000000000002
|
1232 |
+
- type: precision_at_5
|
1233 |
+
value: 16.676
|
1234 |
+
- type: recall_at_1
|
1235 |
+
value: 50.916
|
1236 |
+
- type: recall_at_10
|
1237 |
+
value: 82.04
|
1238 |
+
- type: recall_at_100
|
1239 |
+
value: 91.689
|
1240 |
+
- type: recall_at_1000
|
1241 |
+
value: 95.34899999999999
|
1242 |
+
- type: recall_at_3
|
1243 |
+
value: 70.512
|
1244 |
+
- type: recall_at_5
|
1245 |
+
value: 76.29899999999999
|
1246 |
+
- task:
|
1247 |
+
type: Retrieval
|
1248 |
+
dataset:
|
1249 |
+
type: fiqa
|
1250 |
+
name: MTEB FiQA2018
|
1251 |
+
metrics:
|
1252 |
+
- type: map_at_1
|
1253 |
+
value: 13.568
|
1254 |
+
- type: map_at_10
|
1255 |
+
value: 23.264000000000003
|
1256 |
+
- type: map_at_100
|
1257 |
+
value: 24.823999999999998
|
1258 |
+
- type: map_at_1000
|
1259 |
+
value: 25.013999999999996
|
1260 |
+
- type: map_at_3
|
1261 |
+
value: 19.724
|
1262 |
+
- type: map_at_5
|
1263 |
+
value: 21.772
|
1264 |
+
- type: mrr_at_1
|
1265 |
+
value: 27.315
|
1266 |
+
- type: mrr_at_10
|
1267 |
+
value: 35.935
|
1268 |
+
- type: mrr_at_100
|
1269 |
+
value: 36.929
|
1270 |
+
- type: mrr_at_1000
|
1271 |
+
value: 36.985
|
1272 |
+
- type: mrr_at_3
|
1273 |
+
value: 33.591
|
1274 |
+
- type: mrr_at_5
|
1275 |
+
value: 34.848
|
1276 |
+
- type: ndcg_at_1
|
1277 |
+
value: 27.315
|
1278 |
+
- type: ndcg_at_10
|
1279 |
+
value: 29.988
|
1280 |
+
- type: ndcg_at_100
|
1281 |
+
value: 36.41
|
1282 |
+
- type: ndcg_at_1000
|
1283 |
+
value: 40.184999999999995
|
1284 |
+
- type: ndcg_at_3
|
1285 |
+
value: 26.342
|
1286 |
+
- type: ndcg_at_5
|
1287 |
+
value: 27.68
|
1288 |
+
- type: precision_at_1
|
1289 |
+
value: 27.315
|
1290 |
+
- type: precision_at_10
|
1291 |
+
value: 8.565000000000001
|
1292 |
+
- type: precision_at_100
|
1293 |
+
value: 1.508
|
1294 |
+
- type: precision_at_1000
|
1295 |
+
value: 0.219
|
1296 |
+
- type: precision_at_3
|
1297 |
+
value: 17.849999999999998
|
1298 |
+
- type: precision_at_5
|
1299 |
+
value: 13.672999999999998
|
1300 |
+
- type: recall_at_1
|
1301 |
+
value: 13.568
|
1302 |
+
- type: recall_at_10
|
1303 |
+
value: 37.133
|
1304 |
+
- type: recall_at_100
|
1305 |
+
value: 61.475
|
1306 |
+
- type: recall_at_1000
|
1307 |
+
value: 84.372
|
1308 |
+
- type: recall_at_3
|
1309 |
+
value: 24.112000000000002
|
1310 |
+
- type: recall_at_5
|
1311 |
+
value: 29.507
|
1312 |
+
- task:
|
1313 |
+
type: Retrieval
|
1314 |
+
dataset:
|
1315 |
+
type: hotpotqa
|
1316 |
+
name: MTEB HotpotQA
|
1317 |
+
metrics:
|
1318 |
+
- type: map_at_1
|
1319 |
+
value: 30.878
|
1320 |
+
- type: map_at_10
|
1321 |
+
value: 40.868
|
1322 |
+
- type: map_at_100
|
1323 |
+
value: 41.693999999999996
|
1324 |
+
- type: map_at_1000
|
1325 |
+
value: 41.775
|
1326 |
+
- type: map_at_3
|
1327 |
+
value: 38.56
|
1328 |
+
- type: map_at_5
|
1329 |
+
value: 39.947
|
1330 |
+
- type: mrr_at_1
|
1331 |
+
value: 61.756
|
1332 |
+
- type: mrr_at_10
|
1333 |
+
value: 68.265
|
1334 |
+
- type: mrr_at_100
|
1335 |
+
value: 68.671
|
1336 |
+
- type: mrr_at_1000
|
1337 |
+
value: 68.694
|
1338 |
+
- type: mrr_at_3
|
1339 |
+
value: 66.78399999999999
|
1340 |
+
- type: mrr_at_5
|
1341 |
+
value: 67.704
|
1342 |
+
- type: ndcg_at_1
|
1343 |
+
value: 61.756
|
1344 |
+
- type: ndcg_at_10
|
1345 |
+
value: 49.931
|
1346 |
+
- type: ndcg_at_100
|
1347 |
+
value: 53.179
|
1348 |
+
- type: ndcg_at_1000
|
1349 |
+
value: 54.94799999999999
|
1350 |
+
- type: ndcg_at_3
|
1351 |
+
value: 46.103
|
1352 |
+
- type: ndcg_at_5
|
1353 |
+
value: 48.147
|
1354 |
+
- type: precision_at_1
|
1355 |
+
value: 61.756
|
1356 |
+
- type: precision_at_10
|
1357 |
+
value: 10.163
|
1358 |
+
- type: precision_at_100
|
1359 |
+
value: 1.2710000000000001
|
1360 |
+
- type: precision_at_1000
|
1361 |
+
value: 0.151
|
1362 |
+
- type: precision_at_3
|
1363 |
+
value: 28.179
|
1364 |
+
- type: precision_at_5
|
1365 |
+
value: 18.528
|
1366 |
+
- type: recall_at_1
|
1367 |
+
value: 30.878
|
1368 |
+
- type: recall_at_10
|
1369 |
+
value: 50.817
|
1370 |
+
- type: recall_at_100
|
1371 |
+
value: 63.544999999999995
|
1372 |
+
- type: recall_at_1000
|
1373 |
+
value: 75.361
|
1374 |
+
- type: recall_at_3
|
1375 |
+
value: 42.269
|
1376 |
+
- type: recall_at_5
|
1377 |
+
value: 46.32
|
1378 |
+
- task:
|
1379 |
+
type: Classification
|
1380 |
+
dataset:
|
1381 |
+
type: mteb/imdb
|
1382 |
+
name: MTEB ImdbClassification
|
1383 |
+
metrics:
|
1384 |
+
- type: accuracy
|
1385 |
+
value: 64.04799999999999
|
1386 |
+
- type: ap
|
1387 |
+
value: 59.185251455339284
|
1388 |
+
- type: f1
|
1389 |
+
value: 63.947123181349255
|
1390 |
+
- task:
|
1391 |
+
type: Retrieval
|
1392 |
+
dataset:
|
1393 |
+
type: msmarco
|
1394 |
+
name: MTEB MSMARCO
|
1395 |
+
metrics:
|
1396 |
+
- type: map_at_1
|
1397 |
+
value: 18.9
|
1398 |
+
- type: map_at_10
|
1399 |
+
value: 29.748
|
1400 |
+
- type: map_at_100
|
1401 |
+
value: 30.976
|
1402 |
+
- type: map_at_1000
|
1403 |
+
value: 31.041
|
1404 |
+
- type: map_at_3
|
1405 |
+
value: 26.112999999999996
|
1406 |
+
- type: map_at_5
|
1407 |
+
value: 28.197
|
1408 |
+
- type: mrr_at_1
|
1409 |
+
value: 19.413
|
1410 |
+
- type: mrr_at_10
|
1411 |
+
value: 30.322
|
1412 |
+
- type: mrr_at_100
|
1413 |
+
value: 31.497000000000003
|
1414 |
+
- type: mrr_at_1000
|
1415 |
+
value: 31.555
|
1416 |
+
- type: mrr_at_3
|
1417 |
+
value: 26.729000000000003
|
1418 |
+
- type: mrr_at_5
|
1419 |
+
value: 28.788999999999998
|
1420 |
+
- type: ndcg_at_1
|
1421 |
+
value: 19.413
|
1422 |
+
- type: ndcg_at_10
|
1423 |
+
value: 36.048
|
1424 |
+
- type: ndcg_at_100
|
1425 |
+
value: 42.152
|
1426 |
+
- type: ndcg_at_1000
|
1427 |
+
value: 43.772
|
1428 |
+
- type: ndcg_at_3
|
1429 |
+
value: 28.642
|
1430 |
+
- type: ndcg_at_5
|
1431 |
+
value: 32.358
|
1432 |
+
- type: precision_at_1
|
1433 |
+
value: 19.413
|
1434 |
+
- type: precision_at_10
|
1435 |
+
value: 5.785
|
1436 |
+
- type: precision_at_100
|
1437 |
+
value: 0.8869999999999999
|
1438 |
+
- type: precision_at_1000
|
1439 |
+
value: 0.10300000000000001
|
1440 |
+
- type: precision_at_3
|
1441 |
+
value: 12.192
|
1442 |
+
- type: precision_at_5
|
1443 |
+
value: 9.189
|
1444 |
+
- type: recall_at_1
|
1445 |
+
value: 18.9
|
1446 |
+
- type: recall_at_10
|
1447 |
+
value: 55.457
|
1448 |
+
- type: recall_at_100
|
1449 |
+
value: 84.09100000000001
|
1450 |
+
- type: recall_at_1000
|
1451 |
+
value: 96.482
|
1452 |
+
- type: recall_at_3
|
1453 |
+
value: 35.359
|
1454 |
+
- type: recall_at_5
|
1455 |
+
value: 44.275
|
1456 |
+
- task:
|
1457 |
+
type: Classification
|
1458 |
+
dataset:
|
1459 |
+
type: mteb/mtop_domain
|
1460 |
+
name: MTEB MTOPDomainClassification (en)
|
1461 |
+
metrics:
|
1462 |
+
- type: accuracy
|
1463 |
+
value: 92.07706338349293
|
1464 |
+
- type: f1
|
1465 |
+
value: 91.56680443236652
|
1466 |
+
- task:
|
1467 |
+
type: Classification
|
1468 |
+
dataset:
|
1469 |
+
type: mteb/mtop_intent
|
1470 |
+
name: MTEB MTOPIntentClassification (en)
|
1471 |
+
metrics:
|
1472 |
+
- type: accuracy
|
1473 |
+
value: 71.18559051527589
|
1474 |
+
- type: f1
|
1475 |
+
value: 52.42887061726789
|
1476 |
+
- task:
|
1477 |
+
type: Classification
|
1478 |
+
dataset:
|
1479 |
+
type: mteb/amazon_massive_intent
|
1480 |
+
name: MTEB MassiveIntentClassification (en)
|
1481 |
+
metrics:
|
1482 |
+
- type: accuracy
|
1483 |
+
value: 68.64828513786148
|
1484 |
+
- type: f1
|
1485 |
+
value: 66.54281381596097
|
1486 |
+
- task:
|
1487 |
+
type: Classification
|
1488 |
+
dataset:
|
1489 |
+
type: mteb/amazon_massive_scenario
|
1490 |
+
name: MTEB MassiveScenarioClassification (en)
|
1491 |
+
metrics:
|
1492 |
+
- type: accuracy
|
1493 |
+
value: 76.04236718224612
|
1494 |
+
- type: f1
|
1495 |
+
value: 75.89170458655639
|
1496 |
+
- task:
|
1497 |
+
type: Clustering
|
1498 |
+
dataset:
|
1499 |
+
type: mteb/medrxiv-clustering-p2p
|
1500 |
+
name: MTEB MedrxivClusteringP2P
|
1501 |
+
metrics:
|
1502 |
+
- type: v_measure
|
1503 |
+
value: 32.0840369055247
|
1504 |
+
- task:
|
1505 |
+
type: Clustering
|
1506 |
+
dataset:
|
1507 |
+
type: mteb/medrxiv-clustering-s2s
|
1508 |
+
name: MTEB MedrxivClusteringS2S
|
1509 |
+
metrics:
|
1510 |
+
- type: v_measure
|
1511 |
+
value: 29.448729560244537
|
1512 |
+
- task:
|
1513 |
+
type: Reranking
|
1514 |
+
dataset:
|
1515 |
+
type: mteb/mind_small
|
1516 |
+
name: MTEB MindSmallReranking
|
1517 |
+
metrics:
|
1518 |
+
- type: map
|
1519 |
+
value: 31.340856463122375
|
1520 |
+
- type: mrr
|
1521 |
+
value: 32.398547669840916
|
1522 |
+
- task:
|
1523 |
+
type: Retrieval
|
1524 |
+
dataset:
|
1525 |
+
type: nfcorpus
|
1526 |
+
name: MTEB NFCorpus
|
1527 |
+
metrics:
|
1528 |
+
- type: map_at_1
|
1529 |
+
value: 5.526
|
1530 |
+
- type: map_at_10
|
1531 |
+
value: 11.745
|
1532 |
+
- type: map_at_100
|
1533 |
+
value: 14.831
|
1534 |
+
- type: map_at_1000
|
1535 |
+
value: 16.235
|
1536 |
+
- type: map_at_3
|
1537 |
+
value: 8.716
|
1538 |
+
- type: map_at_5
|
1539 |
+
value: 10.101
|
1540 |
+
- type: mrr_at_1
|
1541 |
+
value: 43.653
|
1542 |
+
- type: mrr_at_10
|
1543 |
+
value: 51.06699999999999
|
1544 |
+
- type: mrr_at_100
|
1545 |
+
value: 51.881
|
1546 |
+
- type: mrr_at_1000
|
1547 |
+
value: 51.912000000000006
|
1548 |
+
- type: mrr_at_3
|
1549 |
+
value: 49.02
|
1550 |
+
- type: mrr_at_5
|
1551 |
+
value: 50.288999999999994
|
1552 |
+
- type: ndcg_at_1
|
1553 |
+
value: 41.949999999999996
|
1554 |
+
- type: ndcg_at_10
|
1555 |
+
value: 32.083
|
1556 |
+
- type: ndcg_at_100
|
1557 |
+
value: 30.049999999999997
|
1558 |
+
- type: ndcg_at_1000
|
1559 |
+
value: 38.661
|
1560 |
+
- type: ndcg_at_3
|
1561 |
+
value: 37.940000000000005
|
1562 |
+
- type: ndcg_at_5
|
1563 |
+
value: 35.455999999999996
|
1564 |
+
- type: precision_at_1
|
1565 |
+
value: 43.344
|
1566 |
+
- type: precision_at_10
|
1567 |
+
value: 23.437
|
1568 |
+
- type: precision_at_100
|
1569 |
+
value: 7.829999999999999
|
1570 |
+
- type: precision_at_1000
|
1571 |
+
value: 2.053
|
1572 |
+
- type: precision_at_3
|
1573 |
+
value: 35.501
|
1574 |
+
- type: precision_at_5
|
1575 |
+
value: 30.464000000000002
|
1576 |
+
- type: recall_at_1
|
1577 |
+
value: 5.526
|
1578 |
+
- type: recall_at_10
|
1579 |
+
value: 15.445999999999998
|
1580 |
+
- type: recall_at_100
|
1581 |
+
value: 31.179000000000002
|
1582 |
+
- type: recall_at_1000
|
1583 |
+
value: 61.578
|
1584 |
+
- type: recall_at_3
|
1585 |
+
value: 9.71
|
1586 |
+
- type: recall_at_5
|
1587 |
+
value: 12.026
|
1588 |
+
- task:
|
1589 |
+
type: Retrieval
|
1590 |
+
dataset:
|
1591 |
+
type: nq
|
1592 |
+
name: MTEB NQ
|
1593 |
+
metrics:
|
1594 |
+
- type: map_at_1
|
1595 |
+
value: 23.467
|
1596 |
+
- type: map_at_10
|
1597 |
+
value: 36.041000000000004
|
1598 |
+
- type: map_at_100
|
1599 |
+
value: 37.268
|
1600 |
+
- type: map_at_1000
|
1601 |
+
value: 37.322
|
1602 |
+
- type: map_at_3
|
1603 |
+
value: 32.09
|
1604 |
+
- type: map_at_5
|
1605 |
+
value: 34.414
|
1606 |
+
- type: mrr_at_1
|
1607 |
+
value: 26.738
|
1608 |
+
- type: mrr_at_10
|
1609 |
+
value: 38.665
|
1610 |
+
- type: mrr_at_100
|
1611 |
+
value: 39.64
|
1612 |
+
- type: mrr_at_1000
|
1613 |
+
value: 39.681
|
1614 |
+
- type: mrr_at_3
|
1615 |
+
value: 35.207
|
1616 |
+
- type: mrr_at_5
|
1617 |
+
value: 37.31
|
1618 |
+
- type: ndcg_at_1
|
1619 |
+
value: 26.709
|
1620 |
+
- type: ndcg_at_10
|
1621 |
+
value: 42.942
|
1622 |
+
- type: ndcg_at_100
|
1623 |
+
value: 48.296
|
1624 |
+
- type: ndcg_at_1000
|
1625 |
+
value: 49.651
|
1626 |
+
- type: ndcg_at_3
|
1627 |
+
value: 35.413
|
1628 |
+
- type: ndcg_at_5
|
1629 |
+
value: 39.367999999999995
|
1630 |
+
- type: precision_at_1
|
1631 |
+
value: 26.709
|
1632 |
+
- type: precision_at_10
|
1633 |
+
value: 7.306
|
1634 |
+
- type: precision_at_100
|
1635 |
+
value: 1.0290000000000001
|
1636 |
+
- type: precision_at_1000
|
1637 |
+
value: 0.116
|
1638 |
+
- type: precision_at_3
|
1639 |
+
value: 16.348
|
1640 |
+
- type: precision_at_5
|
1641 |
+
value: 12.068
|
1642 |
+
- type: recall_at_1
|
1643 |
+
value: 23.467
|
1644 |
+
- type: recall_at_10
|
1645 |
+
value: 61.492999999999995
|
1646 |
+
- type: recall_at_100
|
1647 |
+
value: 85.01100000000001
|
1648 |
+
- type: recall_at_1000
|
1649 |
+
value: 95.261
|
1650 |
+
- type: recall_at_3
|
1651 |
+
value: 41.952
|
1652 |
+
- type: recall_at_5
|
1653 |
+
value: 51.105999999999995
|
1654 |
+
- task:
|
1655 |
+
type: Retrieval
|
1656 |
+
dataset:
|
1657 |
+
type: quora
|
1658 |
+
name: MTEB QuoraRetrieval
|
1659 |
+
metrics:
|
1660 |
+
- type: map_at_1
|
1661 |
+
value: 67.51700000000001
|
1662 |
+
- type: map_at_10
|
1663 |
+
value: 81.054
|
1664 |
+
- type: map_at_100
|
1665 |
+
value: 81.727
|
1666 |
+
- type: map_at_1000
|
1667 |
+
value: 81.75200000000001
|
1668 |
+
- type: map_at_3
|
1669 |
+
value: 78.018
|
1670 |
+
- type: map_at_5
|
1671 |
+
value: 79.879
|
1672 |
+
- type: mrr_at_1
|
1673 |
+
value: 77.52
|
1674 |
+
- type: mrr_at_10
|
1675 |
+
value: 84.429
|
1676 |
+
- type: mrr_at_100
|
1677 |
+
value: 84.58200000000001
|
1678 |
+
- type: mrr_at_1000
|
1679 |
+
value: 84.584
|
1680 |
+
- type: mrr_at_3
|
1681 |
+
value: 83.268
|
1682 |
+
- type: mrr_at_5
|
1683 |
+
value: 84.013
|
1684 |
+
- type: ndcg_at_1
|
1685 |
+
value: 77.53
|
1686 |
+
- type: ndcg_at_10
|
1687 |
+
value: 85.277
|
1688 |
+
- type: ndcg_at_100
|
1689 |
+
value: 86.80499999999999
|
1690 |
+
- type: ndcg_at_1000
|
1691 |
+
value: 87.01
|
1692 |
+
- type: ndcg_at_3
|
1693 |
+
value: 81.975
|
1694 |
+
- type: ndcg_at_5
|
1695 |
+
value: 83.723
|
1696 |
+
- type: precision_at_1
|
1697 |
+
value: 77.53
|
1698 |
+
- type: precision_at_10
|
1699 |
+
value: 12.961
|
1700 |
+
- type: precision_at_100
|
1701 |
+
value: 1.502
|
1702 |
+
- type: precision_at_1000
|
1703 |
+
value: 0.156
|
1704 |
+
- type: precision_at_3
|
1705 |
+
value: 35.713
|
1706 |
+
- type: precision_at_5
|
1707 |
+
value: 23.574
|
1708 |
+
- type: recall_at_1
|
1709 |
+
value: 67.51700000000001
|
1710 |
+
- type: recall_at_10
|
1711 |
+
value: 93.486
|
1712 |
+
- type: recall_at_100
|
1713 |
+
value: 98.9
|
1714 |
+
- type: recall_at_1000
|
1715 |
+
value: 99.92999999999999
|
1716 |
+
- type: recall_at_3
|
1717 |
+
value: 84.17999999999999
|
1718 |
+
- type: recall_at_5
|
1719 |
+
value: 88.97500000000001
|
1720 |
+
- task:
|
1721 |
+
type: Clustering
|
1722 |
+
dataset:
|
1723 |
+
type: mteb/reddit-clustering
|
1724 |
+
name: MTEB RedditClustering
|
1725 |
+
metrics:
|
1726 |
+
- type: v_measure
|
1727 |
+
value: 48.225994608749915
|
1728 |
+
- task:
|
1729 |
+
type: Clustering
|
1730 |
+
dataset:
|
1731 |
+
type: mteb/reddit-clustering-p2p
|
1732 |
+
name: MTEB RedditClusteringP2P
|
1733 |
+
metrics:
|
1734 |
+
- type: v_measure
|
1735 |
+
value: 53.17635557157765
|
1736 |
+
- task:
|
1737 |
+
type: Retrieval
|
1738 |
+
dataset:
|
1739 |
+
type: scidocs
|
1740 |
+
name: MTEB SCIDOCS
|
1741 |
+
metrics:
|
1742 |
+
- type: map_at_1
|
1743 |
+
value: 3.988
|
1744 |
+
- type: map_at_10
|
1745 |
+
value: 9.4
|
1746 |
+
- type: map_at_100
|
1747 |
+
value: 10.968
|
1748 |
+
- type: map_at_1000
|
1749 |
+
value: 11.257
|
1750 |
+
- type: map_at_3
|
1751 |
+
value: 7.123
|
1752 |
+
- type: map_at_5
|
1753 |
+
value: 8.221
|
1754 |
+
- type: mrr_at_1
|
1755 |
+
value: 19.7
|
1756 |
+
- type: mrr_at_10
|
1757 |
+
value: 29.098000000000003
|
1758 |
+
- type: mrr_at_100
|
1759 |
+
value: 30.247
|
1760 |
+
- type: mrr_at_1000
|
1761 |
+
value: 30.318
|
1762 |
+
- type: mrr_at_3
|
1763 |
+
value: 26.55
|
1764 |
+
- type: mrr_at_5
|
1765 |
+
value: 27.915
|
1766 |
+
- type: ndcg_at_1
|
1767 |
+
value: 19.7
|
1768 |
+
- type: ndcg_at_10
|
1769 |
+
value: 16.176
|
1770 |
+
- type: ndcg_at_100
|
1771 |
+
value: 22.931
|
1772 |
+
- type: ndcg_at_1000
|
1773 |
+
value: 28.301
|
1774 |
+
- type: ndcg_at_3
|
1775 |
+
value: 16.142
|
1776 |
+
- type: ndcg_at_5
|
1777 |
+
value: 13.633999999999999
|
1778 |
+
- type: precision_at_1
|
1779 |
+
value: 19.7
|
1780 |
+
- type: precision_at_10
|
1781 |
+
value: 8.18
|
1782 |
+
- type: precision_at_100
|
1783 |
+
value: 1.8010000000000002
|
1784 |
+
- type: precision_at_1000
|
1785 |
+
value: 0.309
|
1786 |
+
- type: precision_at_3
|
1787 |
+
value: 15.1
|
1788 |
+
- type: precision_at_5
|
1789 |
+
value: 11.74
|
1790 |
+
- type: recall_at_1
|
1791 |
+
value: 3.988
|
1792 |
+
- type: recall_at_10
|
1793 |
+
value: 16.625
|
1794 |
+
- type: recall_at_100
|
1795 |
+
value: 36.61
|
1796 |
+
- type: recall_at_1000
|
1797 |
+
value: 62.805
|
1798 |
+
- type: recall_at_3
|
1799 |
+
value: 9.168
|
1800 |
+
- type: recall_at_5
|
1801 |
+
value: 11.902
|
1802 |
+
- task:
|
1803 |
+
type: STS
|
1804 |
+
dataset:
|
1805 |
+
type: mteb/sickr-sts
|
1806 |
+
name: MTEB SICK-R
|
1807 |
+
metrics:
|
1808 |
+
- type: cos_sim_pearson
|
1809 |
+
value: 77.29330379162072
|
1810 |
+
- type: cos_sim_spearman
|
1811 |
+
value: 67.22953551111448
|
1812 |
+
- type: euclidean_pearson
|
1813 |
+
value: 71.44682700059415
|
1814 |
+
- type: euclidean_spearman
|
1815 |
+
value: 66.33178012153247
|
1816 |
+
- type: manhattan_pearson
|
1817 |
+
value: 71.46941734657887
|
1818 |
+
- type: manhattan_spearman
|
1819 |
+
value: 66.43234359835814
|
1820 |
+
- task:
|
1821 |
+
type: STS
|
1822 |
+
dataset:
|
1823 |
+
type: mteb/sts12-sts
|
1824 |
+
name: MTEB STS12
|
1825 |
+
metrics:
|
1826 |
+
- type: cos_sim_pearson
|
1827 |
+
value: 75.40943196466576
|
1828 |
+
- type: cos_sim_spearman
|
1829 |
+
value: 66.59241013465915
|
1830 |
+
- type: euclidean_pearson
|
1831 |
+
value: 71.32500540796616
|
1832 |
+
- type: euclidean_spearman
|
1833 |
+
value: 67.86667467202591
|
1834 |
+
- type: manhattan_pearson
|
1835 |
+
value: 71.48209832089134
|
1836 |
+
- type: manhattan_spearman
|
1837 |
+
value: 67.94511626964879
|
1838 |
+
- task:
|
1839 |
+
type: STS
|
1840 |
+
dataset:
|
1841 |
+
type: mteb/sts13-sts
|
1842 |
+
name: MTEB STS13
|
1843 |
+
metrics:
|
1844 |
+
- type: cos_sim_pearson
|
1845 |
+
value: 77.08302398877518
|
1846 |
+
- type: cos_sim_spearman
|
1847 |
+
value: 77.33151317062642
|
1848 |
+
- type: euclidean_pearson
|
1849 |
+
value: 76.77020279715008
|
1850 |
+
- type: euclidean_spearman
|
1851 |
+
value: 77.13893776083225
|
1852 |
+
- type: manhattan_pearson
|
1853 |
+
value: 76.76732290707477
|
1854 |
+
- type: manhattan_spearman
|
1855 |
+
value: 77.14500877396631
|
1856 |
+
- task:
|
1857 |
+
type: STS
|
1858 |
+
dataset:
|
1859 |
+
type: mteb/sts14-sts
|
1860 |
+
name: MTEB STS14
|
1861 |
+
metrics:
|
1862 |
+
- type: cos_sim_pearson
|
1863 |
+
value: 77.46886184932168
|
1864 |
+
- type: cos_sim_spearman
|
1865 |
+
value: 71.82815265534886
|
1866 |
+
- type: euclidean_pearson
|
1867 |
+
value: 75.19783284299076
|
1868 |
+
- type: euclidean_spearman
|
1869 |
+
value: 71.36479611710412
|
1870 |
+
- type: manhattan_pearson
|
1871 |
+
value: 75.30375233959337
|
1872 |
+
- type: manhattan_spearman
|
1873 |
+
value: 71.46280266488021
|
1874 |
+
- task:
|
1875 |
+
type: STS
|
1876 |
+
dataset:
|
1877 |
+
type: mteb/sts15-sts
|
1878 |
+
name: MTEB STS15
|
1879 |
+
metrics:
|
1880 |
+
- type: cos_sim_pearson
|
1881 |
+
value: 80.093017609484
|
1882 |
+
- type: cos_sim_spearman
|
1883 |
+
value: 80.65931167868882
|
1884 |
+
- type: euclidean_pearson
|
1885 |
+
value: 80.36786337117047
|
1886 |
+
- type: euclidean_spearman
|
1887 |
+
value: 81.30521389642827
|
1888 |
+
- type: manhattan_pearson
|
1889 |
+
value: 80.37922433220973
|
1890 |
+
- type: manhattan_spearman
|
1891 |
+
value: 81.30496664496285
|
1892 |
+
- task:
|
1893 |
+
type: STS
|
1894 |
+
dataset:
|
1895 |
+
type: mteb/sts16-sts
|
1896 |
+
name: MTEB STS16
|
1897 |
+
metrics:
|
1898 |
+
- type: cos_sim_pearson
|
1899 |
+
value: 77.98998347238742
|
1900 |
+
- type: cos_sim_spearman
|
1901 |
+
value: 78.91151365939403
|
1902 |
+
- type: euclidean_pearson
|
1903 |
+
value: 76.40510899217841
|
1904 |
+
- type: euclidean_spearman
|
1905 |
+
value: 76.8551459824213
|
1906 |
+
- type: manhattan_pearson
|
1907 |
+
value: 76.3986079603294
|
1908 |
+
- type: manhattan_spearman
|
1909 |
+
value: 76.8848053254288
|
1910 |
+
- task:
|
1911 |
+
type: STS
|
1912 |
+
dataset:
|
1913 |
+
type: mteb/sts17-crosslingual-sts
|
1914 |
+
name: MTEB STS17 (en-en)
|
1915 |
+
metrics:
|
1916 |
+
- type: cos_sim_pearson
|
1917 |
+
value: 85.63510653472044
|
1918 |
+
- type: cos_sim_spearman
|
1919 |
+
value: 86.98674844768605
|
1920 |
+
- type: euclidean_pearson
|
1921 |
+
value: 85.205080538809
|
1922 |
+
- type: euclidean_spearman
|
1923 |
+
value: 85.53630494151886
|
1924 |
+
- type: manhattan_pearson
|
1925 |
+
value: 85.48612469885626
|
1926 |
+
- type: manhattan_spearman
|
1927 |
+
value: 85.81741413931921
|
1928 |
+
- task:
|
1929 |
+
type: STS
|
1930 |
+
dataset:
|
1931 |
+
type: mteb/sts22-crosslingual-sts
|
1932 |
+
name: MTEB STS22 (en)
|
1933 |
+
metrics:
|
1934 |
+
- type: cos_sim_pearson
|
1935 |
+
value: 66.7257987615171
|
1936 |
+
- type: cos_sim_spearman
|
1937 |
+
value: 67.30387805090024
|
1938 |
+
- type: euclidean_pearson
|
1939 |
+
value: 69.46877227885867
|
1940 |
+
- type: euclidean_spearman
|
1941 |
+
value: 69.33161798704344
|
1942 |
+
- type: manhattan_pearson
|
1943 |
+
value: 69.82773311626424
|
1944 |
+
- type: manhattan_spearman
|
1945 |
+
value: 69.57199940498796
|
1946 |
+
- task:
|
1947 |
+
type: STS
|
1948 |
+
dataset:
|
1949 |
+
type: mteb/stsbenchmark-sts
|
1950 |
+
name: MTEB STSBenchmark
|
1951 |
+
metrics:
|
1952 |
+
- type: cos_sim_pearson
|
1953 |
+
value: 79.37322139418472
|
1954 |
+
- type: cos_sim_spearman
|
1955 |
+
value: 77.5887175717799
|
1956 |
+
- type: euclidean_pearson
|
1957 |
+
value: 78.23006410562164
|
1958 |
+
- type: euclidean_spearman
|
1959 |
+
value: 77.18470385673044
|
1960 |
+
- type: manhattan_pearson
|
1961 |
+
value: 78.40868369362455
|
1962 |
+
- type: manhattan_spearman
|
1963 |
+
value: 77.36675823897656
|
1964 |
+
- task:
|
1965 |
+
type: Reranking
|
1966 |
+
dataset:
|
1967 |
+
type: mteb/scidocs-reranking
|
1968 |
+
name: MTEB SciDocsRR
|
1969 |
+
metrics:
|
1970 |
+
- type: map
|
1971 |
+
value: 77.21233007730808
|
1972 |
+
- type: mrr
|
1973 |
+
value: 93.0502386139641
|
1974 |
+
- task:
|
1975 |
+
type: Retrieval
|
1976 |
+
dataset:
|
1977 |
+
type: scifact
|
1978 |
+
name: MTEB SciFact
|
1979 |
+
metrics:
|
1980 |
+
- type: map_at_1
|
1981 |
+
value: 54.567
|
1982 |
+
- type: map_at_10
|
1983 |
+
value: 63.653000000000006
|
1984 |
+
- type: map_at_100
|
1985 |
+
value: 64.282
|
1986 |
+
- type: map_at_1000
|
1987 |
+
value: 64.31099999999999
|
1988 |
+
- type: map_at_3
|
1989 |
+
value: 60.478
|
1990 |
+
- type: map_at_5
|
1991 |
+
value: 62.322
|
1992 |
+
- type: mrr_at_1
|
1993 |
+
value: 56.99999999999999
|
1994 |
+
- type: mrr_at_10
|
1995 |
+
value: 64.759
|
1996 |
+
- type: mrr_at_100
|
1997 |
+
value: 65.274
|
1998 |
+
- type: mrr_at_1000
|
1999 |
+
value: 65.301
|
2000 |
+
- type: mrr_at_3
|
2001 |
+
value: 62.333000000000006
|
2002 |
+
- type: mrr_at_5
|
2003 |
+
value: 63.817
|
2004 |
+
- type: ndcg_at_1
|
2005 |
+
value: 56.99999999999999
|
2006 |
+
- type: ndcg_at_10
|
2007 |
+
value: 68.28699999999999
|
2008 |
+
- type: ndcg_at_100
|
2009 |
+
value: 70.98400000000001
|
2010 |
+
- type: ndcg_at_1000
|
2011 |
+
value: 71.695
|
2012 |
+
- type: ndcg_at_3
|
2013 |
+
value: 62.656
|
2014 |
+
- type: ndcg_at_5
|
2015 |
+
value: 65.523
|
2016 |
+
- type: precision_at_1
|
2017 |
+
value: 56.99999999999999
|
2018 |
+
- type: precision_at_10
|
2019 |
+
value: 9.232999999999999
|
2020 |
+
- type: precision_at_100
|
2021 |
+
value: 1.0630000000000002
|
2022 |
+
- type: precision_at_1000
|
2023 |
+
value: 0.11199999999999999
|
2024 |
+
- type: precision_at_3
|
2025 |
+
value: 24.221999999999998
|
2026 |
+
- type: precision_at_5
|
2027 |
+
value: 16.333000000000002
|
2028 |
+
- type: recall_at_1
|
2029 |
+
value: 54.567
|
2030 |
+
- type: recall_at_10
|
2031 |
+
value: 81.45599999999999
|
2032 |
+
- type: recall_at_100
|
2033 |
+
value: 93.5
|
2034 |
+
- type: recall_at_1000
|
2035 |
+
value: 99.0
|
2036 |
+
- type: recall_at_3
|
2037 |
+
value: 66.228
|
2038 |
+
- type: recall_at_5
|
2039 |
+
value: 73.489
|
2040 |
+
- task:
|
2041 |
+
type: PairClassification
|
2042 |
+
dataset:
|
2043 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2044 |
+
name: MTEB SprintDuplicateQuestions
|
2045 |
+
metrics:
|
2046 |
+
- type: cos_sim_accuracy
|
2047 |
+
value: 99.74455445544554
|
2048 |
+
- type: cos_sim_ap
|
2049 |
+
value: 92.57836032673468
|
2050 |
+
- type: cos_sim_f1
|
2051 |
+
value: 87.0471464019851
|
2052 |
+
- type: cos_sim_precision
|
2053 |
+
value: 86.4039408866995
|
2054 |
+
- type: cos_sim_recall
|
2055 |
+
value: 87.7
|
2056 |
+
- type: dot_accuracy
|
2057 |
+
value: 99.56039603960396
|
2058 |
+
- type: dot_ap
|
2059 |
+
value: 82.47233353407186
|
2060 |
+
- type: dot_f1
|
2061 |
+
value: 76.78207739307537
|
2062 |
+
- type: dot_precision
|
2063 |
+
value: 78.21576763485477
|
2064 |
+
- type: dot_recall
|
2065 |
+
value: 75.4
|
2066 |
+
- type: euclidean_accuracy
|
2067 |
+
value: 99.73069306930694
|
2068 |
+
- type: euclidean_ap
|
2069 |
+
value: 91.70507666665775
|
2070 |
+
- type: euclidean_f1
|
2071 |
+
value: 86.26262626262626
|
2072 |
+
- type: euclidean_precision
|
2073 |
+
value: 87.14285714285714
|
2074 |
+
- type: euclidean_recall
|
2075 |
+
value: 85.39999999999999
|
2076 |
+
- type: manhattan_accuracy
|
2077 |
+
value: 99.73861386138614
|
2078 |
+
- type: manhattan_ap
|
2079 |
+
value: 91.96809459281754
|
2080 |
+
- type: manhattan_f1
|
2081 |
+
value: 86.6
|
2082 |
+
- type: manhattan_precision
|
2083 |
+
value: 86.6
|
2084 |
+
- type: manhattan_recall
|
2085 |
+
value: 86.6
|
2086 |
+
- type: max_accuracy
|
2087 |
+
value: 99.74455445544554
|
2088 |
+
- type: max_ap
|
2089 |
+
value: 92.57836032673468
|
2090 |
+
- type: max_f1
|
2091 |
+
value: 87.0471464019851
|
2092 |
+
- task:
|
2093 |
+
type: Clustering
|
2094 |
+
dataset:
|
2095 |
+
type: mteb/stackexchange-clustering
|
2096 |
+
name: MTEB StackExchangeClustering
|
2097 |
+
metrics:
|
2098 |
+
- type: v_measure
|
2099 |
+
value: 60.85593925770172
|
2100 |
+
- task:
|
2101 |
+
type: Clustering
|
2102 |
+
dataset:
|
2103 |
+
type: mteb/stackexchange-clustering-p2p
|
2104 |
+
name: MTEB StackExchangeClusteringP2P
|
2105 |
+
metrics:
|
2106 |
+
- type: v_measure
|
2107 |
+
value: 32.356772998237496
|
2108 |
+
- task:
|
2109 |
+
type: Reranking
|
2110 |
+
dataset:
|
2111 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2112 |
+
name: MTEB StackOverflowDupQuestions
|
2113 |
+
metrics:
|
2114 |
+
- type: map
|
2115 |
+
value: 49.320607035290735
|
2116 |
+
- type: mrr
|
2117 |
+
value: 50.09196481622952
|
2118 |
+
- task:
|
2119 |
+
type: Summarization
|
2120 |
+
dataset:
|
2121 |
+
type: mteb/summeval
|
2122 |
+
name: MTEB SummEval
|
2123 |
+
metrics:
|
2124 |
+
- type: cos_sim_pearson
|
2125 |
+
value: 25.57602918901377
|
2126 |
+
- type: cos_sim_spearman
|
2127 |
+
value: 25.440272876996694
|
2128 |
+
- type: dot_pearson
|
2129 |
+
value: 24.909680980895065
|
2130 |
+
- type: dot_spearman
|
2131 |
+
value: 24.032627570006824
|
2132 |
+
- task:
|
2133 |
+
type: Retrieval
|
2134 |
+
dataset:
|
2135 |
+
type: trec-covid
|
2136 |
+
name: MTEB TRECCOVID
|
2137 |
+
metrics:
|
2138 |
+
- type: map_at_1
|
2139 |
+
value: 0.22100000000000003
|
2140 |
+
- type: map_at_10
|
2141 |
+
value: 1.7229999999999999
|
2142 |
+
- type: map_at_100
|
2143 |
+
value: 9.195
|
2144 |
+
- type: map_at_1000
|
2145 |
+
value: 21.999
|
2146 |
+
- type: map_at_3
|
2147 |
+
value: 0.6479999999999999
|
2148 |
+
- type: map_at_5
|
2149 |
+
value: 0.964
|
2150 |
+
- type: mrr_at_1
|
2151 |
+
value: 86.0
|
2152 |
+
- type: mrr_at_10
|
2153 |
+
value: 90.667
|
2154 |
+
- type: mrr_at_100
|
2155 |
+
value: 90.858
|
2156 |
+
- type: mrr_at_1000
|
2157 |
+
value: 90.858
|
2158 |
+
- type: mrr_at_3
|
2159 |
+
value: 90.667
|
2160 |
+
- type: mrr_at_5
|
2161 |
+
value: 90.667
|
2162 |
+
- type: ndcg_at_1
|
2163 |
+
value: 82.0
|
2164 |
+
- type: ndcg_at_10
|
2165 |
+
value: 72.98
|
2166 |
+
- type: ndcg_at_100
|
2167 |
+
value: 52.868
|
2168 |
+
- type: ndcg_at_1000
|
2169 |
+
value: 46.541
|
2170 |
+
- type: ndcg_at_3
|
2171 |
+
value: 80.39699999999999
|
2172 |
+
- type: ndcg_at_5
|
2173 |
+
value: 76.303
|
2174 |
+
- type: precision_at_1
|
2175 |
+
value: 86.0
|
2176 |
+
- type: precision_at_10
|
2177 |
+
value: 75.8
|
2178 |
+
- type: precision_at_100
|
2179 |
+
value: 53.5
|
2180 |
+
- type: precision_at_1000
|
2181 |
+
value: 20.946
|
2182 |
+
- type: precision_at_3
|
2183 |
+
value: 85.333
|
2184 |
+
- type: precision_at_5
|
2185 |
+
value: 79.2
|
2186 |
+
- type: recall_at_1
|
2187 |
+
value: 0.22100000000000003
|
2188 |
+
- type: recall_at_10
|
2189 |
+
value: 1.9109999999999998
|
2190 |
+
- type: recall_at_100
|
2191 |
+
value: 12.437
|
2192 |
+
- type: recall_at_1000
|
2193 |
+
value: 43.606
|
2194 |
+
- type: recall_at_3
|
2195 |
+
value: 0.681
|
2196 |
+
- type: recall_at_5
|
2197 |
+
value: 1.023
|
2198 |
+
- task:
|
2199 |
+
type: Retrieval
|
2200 |
+
dataset:
|
2201 |
+
type: webis-touche2020
|
2202 |
+
name: MTEB Touche2020
|
2203 |
+
metrics:
|
2204 |
+
- type: map_at_1
|
2205 |
+
value: 2.5
|
2206 |
+
- type: map_at_10
|
2207 |
+
value: 9.568999999999999
|
2208 |
+
- type: map_at_100
|
2209 |
+
value: 15.653
|
2210 |
+
- type: map_at_1000
|
2211 |
+
value: 17.188
|
2212 |
+
- type: map_at_3
|
2213 |
+
value: 5.335999999999999
|
2214 |
+
- type: map_at_5
|
2215 |
+
value: 6.522
|
2216 |
+
- type: mrr_at_1
|
2217 |
+
value: 34.694
|
2218 |
+
- type: mrr_at_10
|
2219 |
+
value: 49.184
|
2220 |
+
- type: mrr_at_100
|
2221 |
+
value: 50.512
|
2222 |
+
- type: mrr_at_1000
|
2223 |
+
value: 50.512
|
2224 |
+
- type: mrr_at_3
|
2225 |
+
value: 46.259
|
2226 |
+
- type: mrr_at_5
|
2227 |
+
value: 48.299
|
2228 |
+
- type: ndcg_at_1
|
2229 |
+
value: 30.612000000000002
|
2230 |
+
- type: ndcg_at_10
|
2231 |
+
value: 24.45
|
2232 |
+
- type: ndcg_at_100
|
2233 |
+
value: 35.870999999999995
|
2234 |
+
- type: ndcg_at_1000
|
2235 |
+
value: 47.272999999999996
|
2236 |
+
- type: ndcg_at_3
|
2237 |
+
value: 28.528
|
2238 |
+
- type: ndcg_at_5
|
2239 |
+
value: 25.768
|
2240 |
+
- type: precision_at_1
|
2241 |
+
value: 34.694
|
2242 |
+
- type: precision_at_10
|
2243 |
+
value: 21.429000000000002
|
2244 |
+
- type: precision_at_100
|
2245 |
+
value: 7.265000000000001
|
2246 |
+
- type: precision_at_1000
|
2247 |
+
value: 1.504
|
2248 |
+
- type: precision_at_3
|
2249 |
+
value: 29.252
|
2250 |
+
- type: precision_at_5
|
2251 |
+
value: 24.898
|
2252 |
+
- type: recall_at_1
|
2253 |
+
value: 2.5
|
2254 |
+
- type: recall_at_10
|
2255 |
+
value: 15.844
|
2256 |
+
- type: recall_at_100
|
2257 |
+
value: 45.469
|
2258 |
+
- type: recall_at_1000
|
2259 |
+
value: 81.148
|
2260 |
+
- type: recall_at_3
|
2261 |
+
value: 6.496
|
2262 |
+
- type: recall_at_5
|
2263 |
+
value: 8.790000000000001
|
2264 |
+
- task:
|
2265 |
+
type: Classification
|
2266 |
+
dataset:
|
2267 |
+
type: mteb/toxic_conversations_50k
|
2268 |
+
name: MTEB ToxicConversationsClassification
|
2269 |
+
metrics:
|
2270 |
+
- type: accuracy
|
2271 |
+
value: 68.7272
|
2272 |
+
- type: ap
|
2273 |
+
value: 13.156450706152686
|
2274 |
+
- type: f1
|
2275 |
+
value: 52.814703437064395
|
2276 |
+
- task:
|
2277 |
+
type: Classification
|
2278 |
+
dataset:
|
2279 |
+
type: mteb/tweet_sentiment_extraction
|
2280 |
+
name: MTEB TweetSentimentExtractionClassification
|
2281 |
+
metrics:
|
2282 |
+
- type: accuracy
|
2283 |
+
value: 55.6677985285795
|
2284 |
+
- type: f1
|
2285 |
+
value: 55.9373937514999
|
2286 |
+
- task:
|
2287 |
+
type: Clustering
|
2288 |
+
dataset:
|
2289 |
+
type: mteb/twentynewsgroups-clustering
|
2290 |
+
name: MTEB TwentyNewsgroupsClustering
|
2291 |
+
metrics:
|
2292 |
+
- type: v_measure
|
2293 |
+
value: 40.05809562275603
|
2294 |
+
- task:
|
2295 |
+
type: PairClassification
|
2296 |
+
dataset:
|
2297 |
+
type: mteb/twittersemeval2015-pairclassification
|
2298 |
+
name: MTEB TwitterSemEval2015
|
2299 |
+
metrics:
|
2300 |
+
- type: cos_sim_accuracy
|
2301 |
+
value: 82.76807534124099
|
2302 |
+
- type: cos_sim_ap
|
2303 |
+
value: 62.37052608803734
|
2304 |
+
- type: cos_sim_f1
|
2305 |
+
value: 59.077414934916646
|
2306 |
+
- type: cos_sim_precision
|
2307 |
+
value: 52.07326892109501
|
2308 |
+
- type: cos_sim_recall
|
2309 |
+
value: 68.25857519788919
|
2310 |
+
- type: dot_accuracy
|
2311 |
+
value: 80.56267509089825
|
2312 |
+
- type: dot_ap
|
2313 |
+
value: 54.75349561321037
|
2314 |
+
- type: dot_f1
|
2315 |
+
value: 54.75483794372552
|
2316 |
+
- type: dot_precision
|
2317 |
+
value: 49.77336499028707
|
2318 |
+
- type: dot_recall
|
2319 |
+
value: 60.844327176781
|
2320 |
+
- type: euclidean_accuracy
|
2321 |
+
value: 82.476008821601
|
2322 |
+
- type: euclidean_ap
|
2323 |
+
value: 61.17417554210511
|
2324 |
+
- type: euclidean_f1
|
2325 |
+
value: 57.80318696022382
|
2326 |
+
- type: euclidean_precision
|
2327 |
+
value: 53.622207176709544
|
2328 |
+
- type: euclidean_recall
|
2329 |
+
value: 62.69129287598945
|
2330 |
+
- type: manhattan_accuracy
|
2331 |
+
value: 82.48792990403528
|
2332 |
+
- type: manhattan_ap
|
2333 |
+
value: 61.044816292966544
|
2334 |
+
- type: manhattan_f1
|
2335 |
+
value: 58.03033951360462
|
2336 |
+
- type: manhattan_precision
|
2337 |
+
value: 53.36581045172719
|
2338 |
+
- type: manhattan_recall
|
2339 |
+
value: 63.58839050131926
|
2340 |
+
- type: max_accuracy
|
2341 |
+
value: 82.76807534124099
|
2342 |
+
- type: max_ap
|
2343 |
+
value: 62.37052608803734
|
2344 |
+
- type: max_f1
|
2345 |
+
value: 59.077414934916646
|
2346 |
+
- task:
|
2347 |
+
type: PairClassification
|
2348 |
+
dataset:
|
2349 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2350 |
+
name: MTEB TwitterURLCorpus
|
2351 |
+
metrics:
|
2352 |
+
- type: cos_sim_accuracy
|
2353 |
+
value: 87.97881010594946
|
2354 |
+
- type: cos_sim_ap
|
2355 |
+
value: 83.78748636891035
|
2356 |
+
- type: cos_sim_f1
|
2357 |
+
value: 75.94113995691386
|
2358 |
+
- type: cos_sim_precision
|
2359 |
+
value: 72.22029307590805
|
2360 |
+
- type: cos_sim_recall
|
2361 |
+
value: 80.06621496766245
|
2362 |
+
- type: dot_accuracy
|
2363 |
+
value: 85.69294058291614
|
2364 |
+
- type: dot_ap
|
2365 |
+
value: 78.15363722278026
|
2366 |
+
- type: dot_f1
|
2367 |
+
value: 72.08894926888564
|
2368 |
+
- type: dot_precision
|
2369 |
+
value: 67.28959487419075
|
2370 |
+
- type: dot_recall
|
2371 |
+
value: 77.62550046196489
|
2372 |
+
- type: euclidean_accuracy
|
2373 |
+
value: 87.73625179493149
|
2374 |
+
- type: euclidean_ap
|
2375 |
+
value: 83.19012184470559
|
2376 |
+
- type: euclidean_f1
|
2377 |
+
value: 75.5148064623461
|
2378 |
+
- type: euclidean_precision
|
2379 |
+
value: 72.63352535381551
|
2380 |
+
- type: euclidean_recall
|
2381 |
+
value: 78.6341238065907
|
2382 |
+
- type: manhattan_accuracy
|
2383 |
+
value: 87.74013272790779
|
2384 |
+
- type: manhattan_ap
|
2385 |
+
value: 83.23305405113403
|
2386 |
+
- type: manhattan_f1
|
2387 |
+
value: 75.63960775639607
|
2388 |
+
- type: manhattan_precision
|
2389 |
+
value: 72.563304569246
|
2390 |
+
- type: manhattan_recall
|
2391 |
+
value: 78.9882968894364
|
2392 |
+
- type: max_accuracy
|
2393 |
+
value: 87.97881010594946
|
2394 |
+
- type: max_ap
|
2395 |
+
value: 83.78748636891035
|
2396 |
+
- type: max_f1
|
2397 |
+
value: 75.94113995691386
|
2398 |
---
|
2399 |
|
2400 |
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit
|