Commit
•
4b93744
1
Parent(s):
4d9e730
Add new SentenceTransformer model.
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +10 -0
- README.md +1216 -0
- config.json +28 -0
- config_sentence_transformers.json +10 -0
- model.safetensors +3 -0
- modules.json +20 -0
- sentence_bert_config.json +4 -0
- sentencepiece.bpe.model +3 -0
- special_tokens_map.json +51 -0
- tokenizer.json +3 -0
- tokenizer_config.json +55 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
1_Pooling/config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"word_embedding_dimension": 1024,
|
| 3 |
+
"pooling_mode_cls_token": true,
|
| 4 |
+
"pooling_mode_mean_tokens": false,
|
| 5 |
+
"pooling_mode_max_tokens": false,
|
| 6 |
+
"pooling_mode_mean_sqrt_len_tokens": false,
|
| 7 |
+
"pooling_mode_weightedmean_tokens": false,
|
| 8 |
+
"pooling_mode_lasttoken": false,
|
| 9 |
+
"include_prompt": true
|
| 10 |
+
}
|
README.md
ADDED
|
@@ -0,0 +1,1216 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
base_model: BAAI/bge-m3
|
| 3 |
+
datasets: []
|
| 4 |
+
language: []
|
| 5 |
+
library_name: sentence-transformers
|
| 6 |
+
metrics:
|
| 7 |
+
- cosine_accuracy@1
|
| 8 |
+
- cosine_accuracy@3
|
| 9 |
+
- cosine_accuracy@5
|
| 10 |
+
- cosine_accuracy@10
|
| 11 |
+
- cosine_precision@1
|
| 12 |
+
- cosine_precision@3
|
| 13 |
+
- cosine_precision@5
|
| 14 |
+
- cosine_precision@10
|
| 15 |
+
- cosine_recall@1
|
| 16 |
+
- cosine_recall@3
|
| 17 |
+
- cosine_recall@5
|
| 18 |
+
- cosine_recall@10
|
| 19 |
+
- cosine_ndcg@10
|
| 20 |
+
- cosine_mrr@10
|
| 21 |
+
- cosine_map@100
|
| 22 |
+
pipeline_tag: sentence-similarity
|
| 23 |
+
tags:
|
| 24 |
+
- sentence-transformers
|
| 25 |
+
- sentence-similarity
|
| 26 |
+
- feature-extraction
|
| 27 |
+
- generated_from_trainer
|
| 28 |
+
- dataset_size:4173
|
| 29 |
+
- loss:MatryoshkaLoss
|
| 30 |
+
- loss:MultipleNegativesRankingLoss
|
| 31 |
+
widget:
|
| 32 |
+
- source_sentence: Si dins el termini que s'hagi atorgat amb aquesta finalitat els
|
| 33 |
+
habitatges que en disposen no s'han adaptat, la llicència pot ésser revocada.
|
| 34 |
+
sentences:
|
| 35 |
+
- Qui pot sol·licitar la pròrroga de la prestació?
|
| 36 |
+
- Quin és el resultat de la constatació dels fets denunciats per part de l'Ajuntament?
|
| 37 |
+
- Què passa si no s'adapten els habitatges d'ús turístic dins el termini establert?
|
| 38 |
+
- source_sentence: En cas que a la sepultura hi hagi despulles, la persona titular
|
| 39 |
+
podrà triar entre traslladar-les a una altra sepultura de la què en sigui el/la
|
| 40 |
+
titular o bé que l'Ajuntament les traslladi a l'ossera general.
|
| 41 |
+
sentences:
|
| 42 |
+
- Què passa amb les despulles si la persona titular decideix traslladar-les a una
|
| 43 |
+
altra sepultura?
|
| 44 |
+
- Quins són els beneficis de la llicència de publicitat dinàmica?
|
| 45 |
+
- Quan es va aprovar els models d'aval per part de la Junta de Govern Local?
|
| 46 |
+
- source_sentence: La colònia felina té un paper important en la reducció del nombre
|
| 47 |
+
d'animals abandonats, ja que proporciona un refugi segur i un entorn adequat per
|
| 48 |
+
als animals que es troben en situació de risc o abandonament.
|
| 49 |
+
sentences:
|
| 50 |
+
- Quin és el termini per justificar la realització del projecte/activitat subvencionada?
|
| 51 |
+
- Quins són els tractaments mèdics que beneficien la salut de l'empleat municipal?
|
| 52 |
+
- Quin és el paper de la colònia felina en la reducció del nombre d'animals abandonats?
|
| 53 |
+
- source_sentence: 'La realització de les obres que s’indiquen a continuació està
|
| 54 |
+
subjecta a l’obtenció d’una llicència d’obra major atorgada per l’Ajuntament:
|
| 55 |
+
... Compartimentació de naus industrials existents...'
|
| 56 |
+
sentences:
|
| 57 |
+
- Quin tipus d’obra es refereix a la compartimentació de naus industrials existents?
|
| 58 |
+
- Quin és el benefici principal del tràmit de canvi de titular de la llicència de
|
| 59 |
+
gual?
|
| 60 |
+
- Quin és el tipus de garantia que es pot fer mitjançant una assegurança de caució?
|
| 61 |
+
- source_sentence: Els membres de la Corporació tenen dret a obtenir dels òrgans de
|
| 62 |
+
l'Ajuntament les dades o informacions...
|
| 63 |
+
sentences:
|
| 64 |
+
- Quin és el paper dels òrgans de l'Ajuntament en relació amb les sol·licituds dels
|
| 65 |
+
membres de la Corporació?
|
| 66 |
+
- Quin és el motiu principal perquè un beneficiari pugui perdre el dret a una subvenció?
|
| 67 |
+
- Quin és el benefici de la presentació de recursos?
|
| 68 |
+
model-index:
|
| 69 |
+
- name: SentenceTransformer based on BAAI/bge-m3
|
| 70 |
+
results:
|
| 71 |
+
- task:
|
| 72 |
+
type: information-retrieval
|
| 73 |
+
name: Information Retrieval
|
| 74 |
+
dataset:
|
| 75 |
+
name: dim 768
|
| 76 |
+
type: dim_768
|
| 77 |
+
metrics:
|
| 78 |
+
- type: cosine_accuracy@1
|
| 79 |
+
value: 0.07543103448275862
|
| 80 |
+
name: Cosine Accuracy@1
|
| 81 |
+
- type: cosine_accuracy@3
|
| 82 |
+
value: 0.14439655172413793
|
| 83 |
+
name: Cosine Accuracy@3
|
| 84 |
+
- type: cosine_accuracy@5
|
| 85 |
+
value: 0.21336206896551724
|
| 86 |
+
name: Cosine Accuracy@5
|
| 87 |
+
- type: cosine_accuracy@10
|
| 88 |
+
value: 0.3900862068965517
|
| 89 |
+
name: Cosine Accuracy@10
|
| 90 |
+
- type: cosine_precision@1
|
| 91 |
+
value: 0.07543103448275862
|
| 92 |
+
name: Cosine Precision@1
|
| 93 |
+
- type: cosine_precision@3
|
| 94 |
+
value: 0.048132183908045974
|
| 95 |
+
name: Cosine Precision@3
|
| 96 |
+
- type: cosine_precision@5
|
| 97 |
+
value: 0.04267241379310344
|
| 98 |
+
name: Cosine Precision@5
|
| 99 |
+
- type: cosine_precision@10
|
| 100 |
+
value: 0.039008620689655174
|
| 101 |
+
name: Cosine Precision@10
|
| 102 |
+
- type: cosine_recall@1
|
| 103 |
+
value: 0.07543103448275862
|
| 104 |
+
name: Cosine Recall@1
|
| 105 |
+
- type: cosine_recall@3
|
| 106 |
+
value: 0.14439655172413793
|
| 107 |
+
name: Cosine Recall@3
|
| 108 |
+
- type: cosine_recall@5
|
| 109 |
+
value: 0.21336206896551724
|
| 110 |
+
name: Cosine Recall@5
|
| 111 |
+
- type: cosine_recall@10
|
| 112 |
+
value: 0.3900862068965517
|
| 113 |
+
name: Cosine Recall@10
|
| 114 |
+
- type: cosine_ndcg@10
|
| 115 |
+
value: 0.19775448839983267
|
| 116 |
+
name: Cosine Ndcg@10
|
| 117 |
+
- type: cosine_mrr@10
|
| 118 |
+
value: 0.14087729200875768
|
| 119 |
+
name: Cosine Mrr@10
|
| 120 |
+
- type: cosine_map@100
|
| 121 |
+
value: 0.1670966505747688
|
| 122 |
+
name: Cosine Map@100
|
| 123 |
+
- task:
|
| 124 |
+
type: information-retrieval
|
| 125 |
+
name: Information Retrieval
|
| 126 |
+
dataset:
|
| 127 |
+
name: dim 512
|
| 128 |
+
type: dim_512
|
| 129 |
+
metrics:
|
| 130 |
+
- type: cosine_accuracy@1
|
| 131 |
+
value: 0.07543103448275862
|
| 132 |
+
name: Cosine Accuracy@1
|
| 133 |
+
- type: cosine_accuracy@3
|
| 134 |
+
value: 0.1400862068965517
|
| 135 |
+
name: Cosine Accuracy@3
|
| 136 |
+
- type: cosine_accuracy@5
|
| 137 |
+
value: 0.20905172413793102
|
| 138 |
+
name: Cosine Accuracy@5
|
| 139 |
+
- type: cosine_accuracy@10
|
| 140 |
+
value: 0.3922413793103448
|
| 141 |
+
name: Cosine Accuracy@10
|
| 142 |
+
- type: cosine_precision@1
|
| 143 |
+
value: 0.07543103448275862
|
| 144 |
+
name: Cosine Precision@1
|
| 145 |
+
- type: cosine_precision@3
|
| 146 |
+
value: 0.046695402298850566
|
| 147 |
+
name: Cosine Precision@3
|
| 148 |
+
- type: cosine_precision@5
|
| 149 |
+
value: 0.04181034482758621
|
| 150 |
+
name: Cosine Precision@5
|
| 151 |
+
- type: cosine_precision@10
|
| 152 |
+
value: 0.03922413793103448
|
| 153 |
+
name: Cosine Precision@10
|
| 154 |
+
- type: cosine_recall@1
|
| 155 |
+
value: 0.07543103448275862
|
| 156 |
+
name: Cosine Recall@1
|
| 157 |
+
- type: cosine_recall@3
|
| 158 |
+
value: 0.1400862068965517
|
| 159 |
+
name: Cosine Recall@3
|
| 160 |
+
- type: cosine_recall@5
|
| 161 |
+
value: 0.20905172413793102
|
| 162 |
+
name: Cosine Recall@5
|
| 163 |
+
- type: cosine_recall@10
|
| 164 |
+
value: 0.3922413793103448
|
| 165 |
+
name: Cosine Recall@10
|
| 166 |
+
- type: cosine_ndcg@10
|
| 167 |
+
value: 0.1973388128367381
|
| 168 |
+
name: Cosine Ndcg@10
|
| 169 |
+
- type: cosine_mrr@10
|
| 170 |
+
value: 0.14006910235358525
|
| 171 |
+
name: Cosine Mrr@10
|
| 172 |
+
- type: cosine_map@100
|
| 173 |
+
value: 0.1660059682423787
|
| 174 |
+
name: Cosine Map@100
|
| 175 |
+
- task:
|
| 176 |
+
type: information-retrieval
|
| 177 |
+
name: Information Retrieval
|
| 178 |
+
dataset:
|
| 179 |
+
name: dim 256
|
| 180 |
+
type: dim_256
|
| 181 |
+
metrics:
|
| 182 |
+
- type: cosine_accuracy@1
|
| 183 |
+
value: 0.07112068965517242
|
| 184 |
+
name: Cosine Accuracy@1
|
| 185 |
+
- type: cosine_accuracy@3
|
| 186 |
+
value: 0.14439655172413793
|
| 187 |
+
name: Cosine Accuracy@3
|
| 188 |
+
- type: cosine_accuracy@5
|
| 189 |
+
value: 0.20905172413793102
|
| 190 |
+
name: Cosine Accuracy@5
|
| 191 |
+
- type: cosine_accuracy@10
|
| 192 |
+
value: 0.3793103448275862
|
| 193 |
+
name: Cosine Accuracy@10
|
| 194 |
+
- type: cosine_precision@1
|
| 195 |
+
value: 0.07112068965517242
|
| 196 |
+
name: Cosine Precision@1
|
| 197 |
+
- type: cosine_precision@3
|
| 198 |
+
value: 0.048132183908045974
|
| 199 |
+
name: Cosine Precision@3
|
| 200 |
+
- type: cosine_precision@5
|
| 201 |
+
value: 0.04181034482758621
|
| 202 |
+
name: Cosine Precision@5
|
| 203 |
+
- type: cosine_precision@10
|
| 204 |
+
value: 0.03793103448275861
|
| 205 |
+
name: Cosine Precision@10
|
| 206 |
+
- type: cosine_recall@1
|
| 207 |
+
value: 0.07112068965517242
|
| 208 |
+
name: Cosine Recall@1
|
| 209 |
+
- type: cosine_recall@3
|
| 210 |
+
value: 0.14439655172413793
|
| 211 |
+
name: Cosine Recall@3
|
| 212 |
+
- type: cosine_recall@5
|
| 213 |
+
value: 0.20905172413793102
|
| 214 |
+
name: Cosine Recall@5
|
| 215 |
+
- type: cosine_recall@10
|
| 216 |
+
value: 0.3793103448275862
|
| 217 |
+
name: Cosine Recall@10
|
| 218 |
+
- type: cosine_ndcg@10
|
| 219 |
+
value: 0.19451734912520316
|
| 220 |
+
name: Cosine Ndcg@10
|
| 221 |
+
- type: cosine_mrr@10
|
| 222 |
+
value: 0.13957307060755345
|
| 223 |
+
name: Cosine Mrr@10
|
| 224 |
+
- type: cosine_map@100
|
| 225 |
+
value: 0.1658323397622155
|
| 226 |
+
name: Cosine Map@100
|
| 227 |
+
- task:
|
| 228 |
+
type: information-retrieval
|
| 229 |
+
name: Information Retrieval
|
| 230 |
+
dataset:
|
| 231 |
+
name: dim 128
|
| 232 |
+
type: dim_128
|
| 233 |
+
metrics:
|
| 234 |
+
- type: cosine_accuracy@1
|
| 235 |
+
value: 0.06465517241379311
|
| 236 |
+
name: Cosine Accuracy@1
|
| 237 |
+
- type: cosine_accuracy@3
|
| 238 |
+
value: 0.13793103448275862
|
| 239 |
+
name: Cosine Accuracy@3
|
| 240 |
+
- type: cosine_accuracy@5
|
| 241 |
+
value: 0.21336206896551724
|
| 242 |
+
name: Cosine Accuracy@5
|
| 243 |
+
- type: cosine_accuracy@10
|
| 244 |
+
value: 0.3577586206896552
|
| 245 |
+
name: Cosine Accuracy@10
|
| 246 |
+
- type: cosine_precision@1
|
| 247 |
+
value: 0.06465517241379311
|
| 248 |
+
name: Cosine Precision@1
|
| 249 |
+
- type: cosine_precision@3
|
| 250 |
+
value: 0.04597701149425287
|
| 251 |
+
name: Cosine Precision@3
|
| 252 |
+
- type: cosine_precision@5
|
| 253 |
+
value: 0.04267241379310345
|
| 254 |
+
name: Cosine Precision@5
|
| 255 |
+
- type: cosine_precision@10
|
| 256 |
+
value: 0.03577586206896552
|
| 257 |
+
name: Cosine Precision@10
|
| 258 |
+
- type: cosine_recall@1
|
| 259 |
+
value: 0.06465517241379311
|
| 260 |
+
name: Cosine Recall@1
|
| 261 |
+
- type: cosine_recall@3
|
| 262 |
+
value: 0.13793103448275862
|
| 263 |
+
name: Cosine Recall@3
|
| 264 |
+
- type: cosine_recall@5
|
| 265 |
+
value: 0.21336206896551724
|
| 266 |
+
name: Cosine Recall@5
|
| 267 |
+
- type: cosine_recall@10
|
| 268 |
+
value: 0.3577586206896552
|
| 269 |
+
name: Cosine Recall@10
|
| 270 |
+
- type: cosine_ndcg@10
|
| 271 |
+
value: 0.18381656342161204
|
| 272 |
+
name: Cosine Ndcg@10
|
| 273 |
+
- type: cosine_mrr@10
|
| 274 |
+
value: 0.13181616037219498
|
| 275 |
+
name: Cosine Mrr@10
|
| 276 |
+
- type: cosine_map@100
|
| 277 |
+
value: 0.15919561658705733
|
| 278 |
+
name: Cosine Map@100
|
| 279 |
+
- task:
|
| 280 |
+
type: information-retrieval
|
| 281 |
+
name: Information Retrieval
|
| 282 |
+
dataset:
|
| 283 |
+
name: dim 64
|
| 284 |
+
type: dim_64
|
| 285 |
+
metrics:
|
| 286 |
+
- type: cosine_accuracy@1
|
| 287 |
+
value: 0.06896551724137931
|
| 288 |
+
name: Cosine Accuracy@1
|
| 289 |
+
- type: cosine_accuracy@3
|
| 290 |
+
value: 0.13577586206896552
|
| 291 |
+
name: Cosine Accuracy@3
|
| 292 |
+
- type: cosine_accuracy@5
|
| 293 |
+
value: 0.20905172413793102
|
| 294 |
+
name: Cosine Accuracy@5
|
| 295 |
+
- type: cosine_accuracy@10
|
| 296 |
+
value: 0.35344827586206895
|
| 297 |
+
name: Cosine Accuracy@10
|
| 298 |
+
- type: cosine_precision@1
|
| 299 |
+
value: 0.06896551724137931
|
| 300 |
+
name: Cosine Precision@1
|
| 301 |
+
- type: cosine_precision@3
|
| 302 |
+
value: 0.04525862068965517
|
| 303 |
+
name: Cosine Precision@3
|
| 304 |
+
- type: cosine_precision@5
|
| 305 |
+
value: 0.041810344827586214
|
| 306 |
+
name: Cosine Precision@5
|
| 307 |
+
- type: cosine_precision@10
|
| 308 |
+
value: 0.03534482758620689
|
| 309 |
+
name: Cosine Precision@10
|
| 310 |
+
- type: cosine_recall@1
|
| 311 |
+
value: 0.06896551724137931
|
| 312 |
+
name: Cosine Recall@1
|
| 313 |
+
- type: cosine_recall@3
|
| 314 |
+
value: 0.13577586206896552
|
| 315 |
+
name: Cosine Recall@3
|
| 316 |
+
- type: cosine_recall@5
|
| 317 |
+
value: 0.20905172413793102
|
| 318 |
+
name: Cosine Recall@5
|
| 319 |
+
- type: cosine_recall@10
|
| 320 |
+
value: 0.35344827586206895
|
| 321 |
+
name: Cosine Recall@10
|
| 322 |
+
- type: cosine_ndcg@10
|
| 323 |
+
value: 0.18256713591724985
|
| 324 |
+
name: Cosine Ndcg@10
|
| 325 |
+
- type: cosine_mrr@10
|
| 326 |
+
value: 0.131704980842912
|
| 327 |
+
name: Cosine Mrr@10
|
| 328 |
+
- type: cosine_map@100
|
| 329 |
+
value: 0.1580121500031178
|
| 330 |
+
name: Cosine Map@100
|
| 331 |
+
---
|
| 332 |
+
|
| 333 |
+
# SentenceTransformer based on BAAI/bge-m3
|
| 334 |
+
|
| 335 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
| 336 |
+
|
| 337 |
+
## Model Details
|
| 338 |
+
|
| 339 |
+
### Model Description
|
| 340 |
+
- **Model Type:** Sentence Transformer
|
| 341 |
+
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
|
| 342 |
+
- **Maximum Sequence Length:** 8192 tokens
|
| 343 |
+
- **Output Dimensionality:** 1024 tokens
|
| 344 |
+
- **Similarity Function:** Cosine Similarity
|
| 345 |
+
<!-- - **Training Dataset:** Unknown -->
|
| 346 |
+
<!-- - **Language:** Unknown -->
|
| 347 |
+
<!-- - **License:** Unknown -->
|
| 348 |
+
|
| 349 |
+
### Model Sources
|
| 350 |
+
|
| 351 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 352 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 353 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 354 |
+
|
| 355 |
+
### Full Model Architecture
|
| 356 |
+
|
| 357 |
+
```
|
| 358 |
+
SentenceTransformer(
|
| 359 |
+
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
|
| 360 |
+
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 361 |
+
(2): Normalize()
|
| 362 |
+
)
|
| 363 |
+
```
|
| 364 |
+
|
| 365 |
+
## Usage
|
| 366 |
+
|
| 367 |
+
### Direct Usage (Sentence Transformers)
|
| 368 |
+
|
| 369 |
+
First install the Sentence Transformers library:
|
| 370 |
+
|
| 371 |
+
```bash
|
| 372 |
+
pip install -U sentence-transformers
|
| 373 |
+
```
|
| 374 |
+
|
| 375 |
+
Then you can load this model and run inference.
|
| 376 |
+
```python
|
| 377 |
+
from sentence_transformers import SentenceTransformer
|
| 378 |
+
|
| 379 |
+
# Download from the 🤗 Hub
|
| 380 |
+
model = SentenceTransformer("adriansanz/sitges2608bai-4ep")
|
| 381 |
+
# Run inference
|
| 382 |
+
sentences = [
|
| 383 |
+
"Els membres de la Corporació tenen dret a obtenir dels òrgans de l'Ajuntament les dades o informacions...",
|
| 384 |
+
"Quin és el paper dels òrgans de l'Ajuntament en relació amb les sol·licituds dels membres de la Corporació?",
|
| 385 |
+
'Quin és el benefici de la presentació de recursos?',
|
| 386 |
+
]
|
| 387 |
+
embeddings = model.encode(sentences)
|
| 388 |
+
print(embeddings.shape)
|
| 389 |
+
# [3, 1024]
|
| 390 |
+
|
| 391 |
+
# Get the similarity scores for the embeddings
|
| 392 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 393 |
+
print(similarities.shape)
|
| 394 |
+
# [3, 3]
|
| 395 |
+
```
|
| 396 |
+
|
| 397 |
+
<!--
|
| 398 |
+
### Direct Usage (Transformers)
|
| 399 |
+
|
| 400 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 401 |
+
|
| 402 |
+
</details>
|
| 403 |
+
-->
|
| 404 |
+
|
| 405 |
+
<!--
|
| 406 |
+
### Downstream Usage (Sentence Transformers)
|
| 407 |
+
|
| 408 |
+
You can finetune this model on your own dataset.
|
| 409 |
+
|
| 410 |
+
<details><summary>Click to expand</summary>
|
| 411 |
+
|
| 412 |
+
</details>
|
| 413 |
+
-->
|
| 414 |
+
|
| 415 |
+
<!--
|
| 416 |
+
### Out-of-Scope Use
|
| 417 |
+
|
| 418 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 419 |
+
-->
|
| 420 |
+
|
| 421 |
+
## Evaluation
|
| 422 |
+
|
| 423 |
+
### Metrics
|
| 424 |
+
|
| 425 |
+
#### Information Retrieval
|
| 426 |
+
* Dataset: `dim_768`
|
| 427 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 428 |
+
|
| 429 |
+
| Metric | Value |
|
| 430 |
+
|:--------------------|:-----------|
|
| 431 |
+
| cosine_accuracy@1 | 0.0754 |
|
| 432 |
+
| cosine_accuracy@3 | 0.1444 |
|
| 433 |
+
| cosine_accuracy@5 | 0.2134 |
|
| 434 |
+
| cosine_accuracy@10 | 0.3901 |
|
| 435 |
+
| cosine_precision@1 | 0.0754 |
|
| 436 |
+
| cosine_precision@3 | 0.0481 |
|
| 437 |
+
| cosine_precision@5 | 0.0427 |
|
| 438 |
+
| cosine_precision@10 | 0.039 |
|
| 439 |
+
| cosine_recall@1 | 0.0754 |
|
| 440 |
+
| cosine_recall@3 | 0.1444 |
|
| 441 |
+
| cosine_recall@5 | 0.2134 |
|
| 442 |
+
| cosine_recall@10 | 0.3901 |
|
| 443 |
+
| cosine_ndcg@10 | 0.1978 |
|
| 444 |
+
| cosine_mrr@10 | 0.1409 |
|
| 445 |
+
| **cosine_map@100** | **0.1671** |
|
| 446 |
+
|
| 447 |
+
#### Information Retrieval
|
| 448 |
+
* Dataset: `dim_512`
|
| 449 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 450 |
+
|
| 451 |
+
| Metric | Value |
|
| 452 |
+
|:--------------------|:----------|
|
| 453 |
+
| cosine_accuracy@1 | 0.0754 |
|
| 454 |
+
| cosine_accuracy@3 | 0.1401 |
|
| 455 |
+
| cosine_accuracy@5 | 0.2091 |
|
| 456 |
+
| cosine_accuracy@10 | 0.3922 |
|
| 457 |
+
| cosine_precision@1 | 0.0754 |
|
| 458 |
+
| cosine_precision@3 | 0.0467 |
|
| 459 |
+
| cosine_precision@5 | 0.0418 |
|
| 460 |
+
| cosine_precision@10 | 0.0392 |
|
| 461 |
+
| cosine_recall@1 | 0.0754 |
|
| 462 |
+
| cosine_recall@3 | 0.1401 |
|
| 463 |
+
| cosine_recall@5 | 0.2091 |
|
| 464 |
+
| cosine_recall@10 | 0.3922 |
|
| 465 |
+
| cosine_ndcg@10 | 0.1973 |
|
| 466 |
+
| cosine_mrr@10 | 0.1401 |
|
| 467 |
+
| **cosine_map@100** | **0.166** |
|
| 468 |
+
|
| 469 |
+
#### Information Retrieval
|
| 470 |
+
* Dataset: `dim_256`
|
| 471 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 472 |
+
|
| 473 |
+
| Metric | Value |
|
| 474 |
+
|:--------------------|:-----------|
|
| 475 |
+
| cosine_accuracy@1 | 0.0711 |
|
| 476 |
+
| cosine_accuracy@3 | 0.1444 |
|
| 477 |
+
| cosine_accuracy@5 | 0.2091 |
|
| 478 |
+
| cosine_accuracy@10 | 0.3793 |
|
| 479 |
+
| cosine_precision@1 | 0.0711 |
|
| 480 |
+
| cosine_precision@3 | 0.0481 |
|
| 481 |
+
| cosine_precision@5 | 0.0418 |
|
| 482 |
+
| cosine_precision@10 | 0.0379 |
|
| 483 |
+
| cosine_recall@1 | 0.0711 |
|
| 484 |
+
| cosine_recall@3 | 0.1444 |
|
| 485 |
+
| cosine_recall@5 | 0.2091 |
|
| 486 |
+
| cosine_recall@10 | 0.3793 |
|
| 487 |
+
| cosine_ndcg@10 | 0.1945 |
|
| 488 |
+
| cosine_mrr@10 | 0.1396 |
|
| 489 |
+
| **cosine_map@100** | **0.1658** |
|
| 490 |
+
|
| 491 |
+
#### Information Retrieval
|
| 492 |
+
* Dataset: `dim_128`
|
| 493 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 494 |
+
|
| 495 |
+
| Metric | Value |
|
| 496 |
+
|:--------------------|:-----------|
|
| 497 |
+
| cosine_accuracy@1 | 0.0647 |
|
| 498 |
+
| cosine_accuracy@3 | 0.1379 |
|
| 499 |
+
| cosine_accuracy@5 | 0.2134 |
|
| 500 |
+
| cosine_accuracy@10 | 0.3578 |
|
| 501 |
+
| cosine_precision@1 | 0.0647 |
|
| 502 |
+
| cosine_precision@3 | 0.046 |
|
| 503 |
+
| cosine_precision@5 | 0.0427 |
|
| 504 |
+
| cosine_precision@10 | 0.0358 |
|
| 505 |
+
| cosine_recall@1 | 0.0647 |
|
| 506 |
+
| cosine_recall@3 | 0.1379 |
|
| 507 |
+
| cosine_recall@5 | 0.2134 |
|
| 508 |
+
| cosine_recall@10 | 0.3578 |
|
| 509 |
+
| cosine_ndcg@10 | 0.1838 |
|
| 510 |
+
| cosine_mrr@10 | 0.1318 |
|
| 511 |
+
| **cosine_map@100** | **0.1592** |
|
| 512 |
+
|
| 513 |
+
#### Information Retrieval
|
| 514 |
+
* Dataset: `dim_64`
|
| 515 |
+
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
| 516 |
+
|
| 517 |
+
| Metric | Value |
|
| 518 |
+
|:--------------------|:----------|
|
| 519 |
+
| cosine_accuracy@1 | 0.069 |
|
| 520 |
+
| cosine_accuracy@3 | 0.1358 |
|
| 521 |
+
| cosine_accuracy@5 | 0.2091 |
|
| 522 |
+
| cosine_accuracy@10 | 0.3534 |
|
| 523 |
+
| cosine_precision@1 | 0.069 |
|
| 524 |
+
| cosine_precision@3 | 0.0453 |
|
| 525 |
+
| cosine_precision@5 | 0.0418 |
|
| 526 |
+
| cosine_precision@10 | 0.0353 |
|
| 527 |
+
| cosine_recall@1 | 0.069 |
|
| 528 |
+
| cosine_recall@3 | 0.1358 |
|
| 529 |
+
| cosine_recall@5 | 0.2091 |
|
| 530 |
+
| cosine_recall@10 | 0.3534 |
|
| 531 |
+
| cosine_ndcg@10 | 0.1826 |
|
| 532 |
+
| cosine_mrr@10 | 0.1317 |
|
| 533 |
+
| **cosine_map@100** | **0.158** |
|
| 534 |
+
|
| 535 |
+
<!--
|
| 536 |
+
## Bias, Risks and Limitations
|
| 537 |
+
|
| 538 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 539 |
+
-->
|
| 540 |
+
|
| 541 |
+
<!--
|
| 542 |
+
### Recommendations
|
| 543 |
+
|
| 544 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 545 |
+
-->
|
| 546 |
+
|
| 547 |
+
## Training Details
|
| 548 |
+
|
| 549 |
+
### Training Dataset
|
| 550 |
+
|
| 551 |
+
#### Unnamed Dataset
|
| 552 |
+
|
| 553 |
+
|
| 554 |
+
* Size: 4,173 training samples
|
| 555 |
+
* Columns: <code>positive</code> and <code>anchor</code>
|
| 556 |
+
* Approximate statistics based on the first 1000 samples:
|
| 557 |
+
| | positive | anchor |
|
| 558 |
+
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
|
| 559 |
+
| type | string | string |
|
| 560 |
+
| details | <ul><li>min: 8 tokens</li><li>mean: 48.65 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.96 tokens</li><li>max: 45 tokens</li></ul> |
|
| 561 |
+
* Samples:
|
| 562 |
+
| positive | anchor |
|
| 563 |
+
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
|
| 564 |
+
| <code>Quan es produeix la caducitat del dret funerari per haver transcorregut el termini de concessió i un cop que l'Ajuntament hagi resolt el procediment legalment establert per a la declaració de caducitat, és imprescindible formalitzar la nova concessió del dret.</code> | <code>Quan es produeix la caducitat del dret funerari?</code> |
|
| 565 |
+
| <code>Les persones beneficiàries de l'ajut per a la creació de noves empreses per persones donades d'alta al règim especial de treballadors autònoms.</code> | <code>Quin és el tipus de persones que poden beneficiar-se de l'ajut?</code> |
|
| 566 |
+
| <code>Les entitats beneficiàries són les responsables de la gestió dels recursos econòmics i materials assignats per a la realització del projecte o activitat subvencionat.</code> | <code>Quin és el paper de les entitats beneficiàries en la gestió dels recursos?</code> |
|
| 567 |
+
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
|
| 568 |
+
```json
|
| 569 |
+
{
|
| 570 |
+
"loss": "MultipleNegativesRankingLoss",
|
| 571 |
+
"matryoshka_dims": [
|
| 572 |
+
768,
|
| 573 |
+
512,
|
| 574 |
+
256,
|
| 575 |
+
128,
|
| 576 |
+
64
|
| 577 |
+
],
|
| 578 |
+
"matryoshka_weights": [
|
| 579 |
+
1,
|
| 580 |
+
1,
|
| 581 |
+
1,
|
| 582 |
+
1,
|
| 583 |
+
1
|
| 584 |
+
],
|
| 585 |
+
"n_dims_per_step": -1
|
| 586 |
+
}
|
| 587 |
+
```
|
| 588 |
+
|
| 589 |
+
### Training Hyperparameters
|
| 590 |
+
#### Non-Default Hyperparameters
|
| 591 |
+
|
| 592 |
+
- `eval_strategy`: epoch
|
| 593 |
+
- `per_device_train_batch_size`: 2
|
| 594 |
+
- `per_device_eval_batch_size`: 2
|
| 595 |
+
- `gradient_accumulation_steps`: 2
|
| 596 |
+
- `learning_rate`: 2e-05
|
| 597 |
+
- `num_train_epochs`: 4
|
| 598 |
+
- `lr_scheduler_type`: cosine
|
| 599 |
+
- `warmup_ratio`: 0.1
|
| 600 |
+
- `bf16`: True
|
| 601 |
+
- `tf32`: False
|
| 602 |
+
- `load_best_model_at_end`: True
|
| 603 |
+
- `optim`: adamw_torch_fused
|
| 604 |
+
- `batch_sampler`: no_duplicates
|
| 605 |
+
|
| 606 |
+
#### All Hyperparameters
|
| 607 |
+
<details><summary>Click to expand</summary>
|
| 608 |
+
|
| 609 |
+
- `overwrite_output_dir`: False
|
| 610 |
+
- `do_predict`: False
|
| 611 |
+
- `eval_strategy`: epoch
|
| 612 |
+
- `prediction_loss_only`: True
|
| 613 |
+
- `per_device_train_batch_size`: 2
|
| 614 |
+
- `per_device_eval_batch_size`: 2
|
| 615 |
+
- `per_gpu_train_batch_size`: None
|
| 616 |
+
- `per_gpu_eval_batch_size`: None
|
| 617 |
+
- `gradient_accumulation_steps`: 2
|
| 618 |
+
- `eval_accumulation_steps`: None
|
| 619 |
+
- `learning_rate`: 2e-05
|
| 620 |
+
- `weight_decay`: 0.0
|
| 621 |
+
- `adam_beta1`: 0.9
|
| 622 |
+
- `adam_beta2`: 0.999
|
| 623 |
+
- `adam_epsilon`: 1e-08
|
| 624 |
+
- `max_grad_norm`: 1.0
|
| 625 |
+
- `num_train_epochs`: 4
|
| 626 |
+
- `max_steps`: -1
|
| 627 |
+
- `lr_scheduler_type`: cosine
|
| 628 |
+
- `lr_scheduler_kwargs`: {}
|
| 629 |
+
- `warmup_ratio`: 0.1
|
| 630 |
+
- `warmup_steps`: 0
|
| 631 |
+
- `log_level`: passive
|
| 632 |
+
- `log_level_replica`: warning
|
| 633 |
+
- `log_on_each_node`: True
|
| 634 |
+
- `logging_nan_inf_filter`: True
|
| 635 |
+
- `save_safetensors`: True
|
| 636 |
+
- `save_on_each_node`: False
|
| 637 |
+
- `save_only_model`: False
|
| 638 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 639 |
+
- `no_cuda`: False
|
| 640 |
+
- `use_cpu`: False
|
| 641 |
+
- `use_mps_device`: False
|
| 642 |
+
- `seed`: 42
|
| 643 |
+
- `data_seed`: None
|
| 644 |
+
- `jit_mode_eval`: False
|
| 645 |
+
- `use_ipex`: False
|
| 646 |
+
- `bf16`: True
|
| 647 |
+
- `fp16`: False
|
| 648 |
+
- `fp16_opt_level`: O1
|
| 649 |
+
- `half_precision_backend`: auto
|
| 650 |
+
- `bf16_full_eval`: False
|
| 651 |
+
- `fp16_full_eval`: False
|
| 652 |
+
- `tf32`: False
|
| 653 |
+
- `local_rank`: 0
|
| 654 |
+
- `ddp_backend`: None
|
| 655 |
+
- `tpu_num_cores`: None
|
| 656 |
+
- `tpu_metrics_debug`: False
|
| 657 |
+
- `debug`: []
|
| 658 |
+
- `dataloader_drop_last`: False
|
| 659 |
+
- `dataloader_num_workers`: 0
|
| 660 |
+
- `dataloader_prefetch_factor`: None
|
| 661 |
+
- `past_index`: -1
|
| 662 |
+
- `disable_tqdm`: False
|
| 663 |
+
- `remove_unused_columns`: True
|
| 664 |
+
- `label_names`: None
|
| 665 |
+
- `load_best_model_at_end`: True
|
| 666 |
+
- `ignore_data_skip`: False
|
| 667 |
+
- `fsdp`: []
|
| 668 |
+
- `fsdp_min_num_params`: 0
|
| 669 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 670 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 671 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 672 |
+
- `deepspeed`: None
|
| 673 |
+
- `label_smoothing_factor`: 0.0
|
| 674 |
+
- `optim`: adamw_torch_fused
|
| 675 |
+
- `optim_args`: None
|
| 676 |
+
- `adafactor`: False
|
| 677 |
+
- `group_by_length`: False
|
| 678 |
+
- `length_column_name`: length
|
| 679 |
+
- `ddp_find_unused_parameters`: None
|
| 680 |
+
- `ddp_bucket_cap_mb`: None
|
| 681 |
+
- `ddp_broadcast_buffers`: False
|
| 682 |
+
- `dataloader_pin_memory`: True
|
| 683 |
+
- `dataloader_persistent_workers`: False
|
| 684 |
+
- `skip_memory_metrics`: True
|
| 685 |
+
- `use_legacy_prediction_loop`: False
|
| 686 |
+
- `push_to_hub`: False
|
| 687 |
+
- `resume_from_checkpoint`: None
|
| 688 |
+
- `hub_model_id`: None
|
| 689 |
+
- `hub_strategy`: every_save
|
| 690 |
+
- `hub_private_repo`: False
|
| 691 |
+
- `hub_always_push`: False
|
| 692 |
+
- `gradient_checkpointing`: False
|
| 693 |
+
- `gradient_checkpointing_kwargs`: None
|
| 694 |
+
- `include_inputs_for_metrics`: False
|
| 695 |
+
- `eval_do_concat_batches`: True
|
| 696 |
+
- `fp16_backend`: auto
|
| 697 |
+
- `push_to_hub_model_id`: None
|
| 698 |
+
- `push_to_hub_organization`: None
|
| 699 |
+
- `mp_parameters`:
|
| 700 |
+
- `auto_find_batch_size`: False
|
| 701 |
+
- `full_determinism`: False
|
| 702 |
+
- `torchdynamo`: None
|
| 703 |
+
- `ray_scope`: last
|
| 704 |
+
- `ddp_timeout`: 1800
|
| 705 |
+
- `torch_compile`: False
|
| 706 |
+
- `torch_compile_backend`: None
|
| 707 |
+
- `torch_compile_mode`: None
|
| 708 |
+
- `dispatch_batches`: None
|
| 709 |
+
- `split_batches`: None
|
| 710 |
+
- `include_tokens_per_second`: False
|
| 711 |
+
- `include_num_input_tokens_seen`: False
|
| 712 |
+
- `neftune_noise_alpha`: None
|
| 713 |
+
- `optim_target_modules`: None
|
| 714 |
+
- `batch_eval_metrics`: False
|
| 715 |
+
- `eval_on_start`: False
|
| 716 |
+
- `batch_sampler`: no_duplicates
|
| 717 |
+
- `multi_dataset_batch_sampler`: proportional
|
| 718 |
+
|
| 719 |
+
</details>
|
| 720 |
+
|
| 721 |
+
### Training Logs
|
| 722 |
+
<details><summary>Click to expand</summary>
|
| 723 |
+
|
| 724 |
+
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|
| 725 |
+
|:----------:|:--------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
|
| 726 |
+
| 0.0096 | 10 | 0.4269 | - | - | - | - | - |
|
| 727 |
+
| 0.0192 | 20 | 0.2328 | - | - | - | - | - |
|
| 728 |
+
| 0.0287 | 30 | 0.2803 | - | - | - | - | - |
|
| 729 |
+
| 0.0383 | 40 | 0.312 | - | - | - | - | - |
|
| 730 |
+
| 0.0479 | 50 | 0.0631 | - | - | - | - | - |
|
| 731 |
+
| 0.0575 | 60 | 0.1824 | - | - | - | - | - |
|
| 732 |
+
| 0.0671 | 70 | 0.3102 | - | - | - | - | - |
|
| 733 |
+
| 0.0767 | 80 | 0.2966 | - | - | - | - | - |
|
| 734 |
+
| 0.0862 | 90 | 0.3715 | - | - | - | - | - |
|
| 735 |
+
| 0.0958 | 100 | 0.0719 | - | - | - | - | - |
|
| 736 |
+
| 0.1054 | 110 | 0.279 | - | - | - | - | - |
|
| 737 |
+
| 0.1150 | 120 | 0.0954 | - | - | - | - | - |
|
| 738 |
+
| 0.1246 | 130 | 0.4912 | - | - | - | - | - |
|
| 739 |
+
| 0.1342 | 140 | 0.2877 | - | - | - | - | - |
|
| 740 |
+
| 0.1437 | 150 | 0.1933 | - | - | - | - | - |
|
| 741 |
+
| 0.1533 | 160 | 0.5942 | - | - | - | - | - |
|
| 742 |
+
| 0.1629 | 170 | 0.1336 | - | - | - | - | - |
|
| 743 |
+
| 0.1725 | 180 | 0.1755 | - | - | - | - | - |
|
| 744 |
+
| 0.1821 | 190 | 0.1455 | - | - | - | - | - |
|
| 745 |
+
| 0.1917 | 200 | 0.4391 | - | - | - | - | - |
|
| 746 |
+
| 0.2012 | 210 | 0.0567 | - | - | - | - | - |
|
| 747 |
+
| 0.2108 | 220 | 0.2368 | - | - | - | - | - |
|
| 748 |
+
| 0.2204 | 230 | 0.0249 | - | - | - | - | - |
|
| 749 |
+
| 0.2300 | 240 | 0.0518 | - | - | - | - | - |
|
| 750 |
+
| 0.2396 | 250 | 0.015 | - | - | - | - | - |
|
| 751 |
+
| 0.2492 | 260 | 0.4096 | - | - | - | - | - |
|
| 752 |
+
| 0.2587 | 270 | 0.115 | - | - | - | - | - |
|
| 753 |
+
| 0.2683 | 280 | 0.0532 | - | - | - | - | - |
|
| 754 |
+
| 0.2779 | 290 | 0.0407 | - | - | - | - | - |
|
| 755 |
+
| 0.2875 | 300 | 0.082 | - | - | - | - | - |
|
| 756 |
+
| 0.2971 | 310 | 0.1086 | - | - | - | - | - |
|
| 757 |
+
| 0.3067 | 320 | 0.0345 | - | - | - | - | - |
|
| 758 |
+
| 0.3162 | 330 | 0.3144 | - | - | - | - | - |
|
| 759 |
+
| 0.3258 | 340 | 0.0056 | - | - | - | - | - |
|
| 760 |
+
| 0.3354 | 350 | 0.0867 | - | - | - | - | - |
|
| 761 |
+
| 0.3450 | 360 | 0.1011 | - | - | - | - | - |
|
| 762 |
+
| 0.3546 | 370 | 0.6417 | - | - | - | - | - |
|
| 763 |
+
| 0.3642 | 380 | 0.0689 | - | - | - | - | - |
|
| 764 |
+
| 0.3737 | 390 | 0.0075 | - | - | - | - | - |
|
| 765 |
+
| 0.3833 | 400 | 0.0822 | - | - | - | - | - |
|
| 766 |
+
| 0.3929 | 410 | 0.098 | - | - | - | - | - |
|
| 767 |
+
| 0.4025 | 420 | 0.0442 | - | - | - | - | - |
|
| 768 |
+
| 0.4121 | 430 | 0.1759 | - | - | - | - | - |
|
| 769 |
+
| 0.4217 | 440 | 0.2625 | - | - | - | - | - |
|
| 770 |
+
| 0.4312 | 450 | 0.1123 | - | - | - | - | - |
|
| 771 |
+
| 0.4408 | 460 | 0.1174 | - | - | - | - | - |
|
| 772 |
+
| 0.4504 | 470 | 0.0529 | - | - | - | - | - |
|
| 773 |
+
| 0.4600 | 480 | 0.5396 | - | - | - | - | - |
|
| 774 |
+
| 0.4696 | 490 | 0.1985 | - | - | - | - | - |
|
| 775 |
+
| 0.4792 | 500 | 0.0016 | - | - | - | - | - |
|
| 776 |
+
| 0.4887 | 510 | 0.0496 | - | - | - | - | - |
|
| 777 |
+
| 0.4983 | 520 | 0.3138 | - | - | - | - | - |
|
| 778 |
+
| 0.5079 | 530 | 0.1974 | - | - | - | - | - |
|
| 779 |
+
| 0.5175 | 540 | 0.3489 | - | - | - | - | - |
|
| 780 |
+
| 0.5271 | 550 | 0.3332 | - | - | - | - | - |
|
| 781 |
+
| 0.5367 | 560 | 0.7838 | - | - | - | - | - |
|
| 782 |
+
| 0.5462 | 570 | 0.8335 | - | - | - | - | - |
|
| 783 |
+
| 0.5558 | 580 | 0.5018 | - | - | - | - | - |
|
| 784 |
+
| 0.5654 | 590 | 0.3391 | - | - | - | - | - |
|
| 785 |
+
| 0.5750 | 600 | 0.0055 | - | - | - | - | - |
|
| 786 |
+
| 0.5846 | 610 | 0.0264 | - | - | - | - | - |
|
| 787 |
+
| 0.5942 | 620 | 0.1397 | - | - | - | - | - |
|
| 788 |
+
| 0.6037 | 630 | 0.1114 | - | - | - | - | - |
|
| 789 |
+
| 0.6133 | 640 | 0.337 | - | - | - | - | - |
|
| 790 |
+
| 0.6229 | 650 | 0.0027 | - | - | - | - | - |
|
| 791 |
+
| 0.6325 | 660 | 0.1454 | - | - | - | - | - |
|
| 792 |
+
| 0.6421 | 670 | 0.2212 | - | - | - | - | - |
|
| 793 |
+
| 0.6517 | 680 | 0.0472 | - | - | - | - | - |
|
| 794 |
+
| 0.6612 | 690 | 0.6882 | - | - | - | - | - |
|
| 795 |
+
| 0.6708 | 700 | 0.0266 | - | - | - | - | - |
|
| 796 |
+
| 0.6804 | 710 | 1.0057 | - | - | - | - | - |
|
| 797 |
+
| 0.6900 | 720 | 0.1456 | - | - | - | - | - |
|
| 798 |
+
| 0.6996 | 730 | 0.4195 | - | - | - | - | - |
|
| 799 |
+
| 0.7092 | 740 | 0.0732 | - | - | - | - | - |
|
| 800 |
+
| 0.7187 | 750 | 0.0588 | - | - | - | - | - |
|
| 801 |
+
| 0.7283 | 760 | 0.0033 | - | - | - | - | - |
|
| 802 |
+
| 0.7379 | 770 | 0.0156 | - | - | - | - | - |
|
| 803 |
+
| 0.7475 | 780 | 0.0997 | - | - | - | - | - |
|
| 804 |
+
| 0.7571 | 790 | 0.856 | - | - | - | - | - |
|
| 805 |
+
| 0.7667 | 800 | 0.2394 | - | - | - | - | - |
|
| 806 |
+
| 0.7762 | 810 | 0.0322 | - | - | - | - | - |
|
| 807 |
+
| 0.7858 | 820 | 0.1821 | - | - | - | - | - |
|
| 808 |
+
| 0.7954 | 830 | 0.1883 | - | - | - | - | - |
|
| 809 |
+
| 0.8050 | 840 | 0.0994 | - | - | - | - | - |
|
| 810 |
+
| 0.8146 | 850 | 0.3889 | - | - | - | - | - |
|
| 811 |
+
| 0.8241 | 860 | 0.0221 | - | - | - | - | - |
|
| 812 |
+
| 0.8337 | 870 | 0.0106 | - | - | - | - | - |
|
| 813 |
+
| 0.8433 | 880 | 0.0031 | - | - | - | - | - |
|
| 814 |
+
| 0.8529 | 890 | 0.1453 | - | - | - | - | - |
|
| 815 |
+
| 0.8625 | 900 | 0.487 | - | - | - | - | - |
|
| 816 |
+
| 0.8721 | 910 | 0.2987 | - | - | - | - | - |
|
| 817 |
+
| 0.8816 | 920 | 0.0347 | - | - | - | - | - |
|
| 818 |
+
| 0.8912 | 930 | 0.2024 | - | - | - | - | - |
|
| 819 |
+
| 0.9008 | 940 | 0.0087 | - | - | - | - | - |
|
| 820 |
+
| 0.9104 | 950 | 0.3944 | - | - | - | - | - |
|
| 821 |
+
| 0.9200 | 960 | 0.0935 | - | - | - | - | - |
|
| 822 |
+
| 0.9296 | 970 | 0.2408 | - | - | - | - | - |
|
| 823 |
+
| 0.9391 | 980 | 0.1545 | - | - | - | - | - |
|
| 824 |
+
| 0.9487 | 990 | 0.1168 | - | - | - | - | - |
|
| 825 |
+
| 0.9583 | 1000 | 0.0051 | - | - | - | - | - |
|
| 826 |
+
| 0.9679 | 1010 | 0.681 | - | - | - | - | - |
|
| 827 |
+
| 0.9775 | 1020 | 0.0198 | - | - | - | - | - |
|
| 828 |
+
| 0.9871 | 1030 | 0.7243 | - | - | - | - | - |
|
| 829 |
+
| 0.9966 | 1040 | 0.0341 | - | - | - | - | - |
|
| 830 |
+
| 0.9995 | 1043 | - | 0.1608 | 0.1639 | 0.1678 | 0.1526 | 0.1610 |
|
| 831 |
+
| 1.0062 | 1050 | 0.001 | - | - | - | - | - |
|
| 832 |
+
| 1.0158 | 1060 | 0.0864 | - | - | - | - | - |
|
| 833 |
+
| 1.0254 | 1070 | 0.0209 | - | - | - | - | - |
|
| 834 |
+
| 1.0350 | 1080 | 0.2703 | - | - | - | - | - |
|
| 835 |
+
| 1.0446 | 1090 | 0.1857 | - | - | - | - | - |
|
| 836 |
+
| 1.0541 | 1100 | 0.0032 | - | - | - | - | - |
|
| 837 |
+
| 1.0637 | 1110 | 0.118 | - | - | - | - | - |
|
| 838 |
+
| 1.0733 | 1120 | 0.0029 | - | - | - | - | - |
|
| 839 |
+
| 1.0829 | 1130 | 0.0393 | - | - | - | - | - |
|
| 840 |
+
| 1.0925 | 1140 | 0.3103 | - | - | - | - | - |
|
| 841 |
+
| 1.1021 | 1150 | 0.0323 | - | - | - | - | - |
|
| 842 |
+
| 1.1116 | 1160 | 0.0925 | - | - | - | - | - |
|
| 843 |
+
| 1.1212 | 1170 | 0.0963 | - | - | - | - | - |
|
| 844 |
+
| 1.1308 | 1180 | 0.0481 | - | - | - | - | - |
|
| 845 |
+
| 1.1404 | 1190 | 0.0396 | - | - | - | - | - |
|
| 846 |
+
| 1.1500 | 1200 | 0.0033 | - | - | - | - | - |
|
| 847 |
+
| 1.1596 | 1210 | 0.1555 | - | - | - | - | - |
|
| 848 |
+
| 1.1691 | 1220 | 0.0938 | - | - | - | - | - |
|
| 849 |
+
| 1.1787 | 1230 | 0.1347 | - | - | - | - | - |
|
| 850 |
+
| 1.1883 | 1240 | 0.3057 | - | - | - | - | - |
|
| 851 |
+
| 1.1979 | 1250 | 0.0005 | - | - | - | - | - |
|
| 852 |
+
| 1.2075 | 1260 | 0.0634 | - | - | - | - | - |
|
| 853 |
+
| 1.2171 | 1270 | 0.0013 | - | - | - | - | - |
|
| 854 |
+
| 1.2266 | 1280 | 0.0012 | - | - | - | - | - |
|
| 855 |
+
| 1.2362 | 1290 | 0.0119 | - | - | - | - | - |
|
| 856 |
+
| 1.2458 | 1300 | 0.002 | - | - | - | - | - |
|
| 857 |
+
| 1.2554 | 1310 | 0.016 | - | - | - | - | - |
|
| 858 |
+
| 1.2650 | 1320 | 0.0169 | - | - | - | - | - |
|
| 859 |
+
| 1.2746 | 1330 | 0.0332 | - | - | - | - | - |
|
| 860 |
+
| 1.2841 | 1340 | 0.0076 | - | - | - | - | - |
|
| 861 |
+
| 1.2937 | 1350 | 0.0029 | - | - | - | - | - |
|
| 862 |
+
| 1.3033 | 1360 | 0.0011 | - | - | - | - | - |
|
| 863 |
+
| 1.3129 | 1370 | 0.0477 | - | - | - | - | - |
|
| 864 |
+
| 1.3225 | 1380 | 0.014 | - | - | - | - | - |
|
| 865 |
+
| 1.3321 | 1390 | 0.0002 | - | - | - | - | - |
|
| 866 |
+
| 1.3416 | 1400 | 0.012 | - | - | - | - | - |
|
| 867 |
+
| 1.3512 | 1410 | 0.0175 | - | - | - | - | - |
|
| 868 |
+
| 1.3608 | 1420 | 0.0088 | - | - | - | - | - |
|
| 869 |
+
| 1.3704 | 1430 | 0.0022 | - | - | - | - | - |
|
| 870 |
+
| 1.3800 | 1440 | 0.0007 | - | - | - | - | - |
|
| 871 |
+
| 1.3896 | 1450 | 0.0098 | - | - | - | - | - |
|
| 872 |
+
| 1.3991 | 1460 | 0.0003 | - | - | - | - | - |
|
| 873 |
+
| 1.4087 | 1470 | 0.0804 | - | - | - | - | - |
|
| 874 |
+
| 1.4183 | 1480 | 0.0055 | - | - | - | - | - |
|
| 875 |
+
| 1.4279 | 1490 | 0.1131 | - | - | - | - | - |
|
| 876 |
+
| 1.4375 | 1500 | 0.0018 | - | - | - | - | - |
|
| 877 |
+
| 1.4471 | 1510 | 0.0002 | - | - | - | - | - |
|
| 878 |
+
| 1.4566 | 1520 | 0.0143 | - | - | - | - | - |
|
| 879 |
+
| 1.4662 | 1530 | 0.0876 | - | - | - | - | - |
|
| 880 |
+
| 1.4758 | 1540 | 0.003 | - | - | - | - | - |
|
| 881 |
+
| 1.4854 | 1550 | 0.0087 | - | - | - | - | - |
|
| 882 |
+
| 1.4950 | 1560 | 0.0005 | - | - | - | - | - |
|
| 883 |
+
| 1.5046 | 1570 | 0.0002 | - | - | - | - | - |
|
| 884 |
+
| 1.5141 | 1580 | 0.1614 | - | - | - | - | - |
|
| 885 |
+
| 1.5237 | 1590 | 0.0017 | - | - | - | - | - |
|
| 886 |
+
| 1.5333 | 1600 | 0.0013 | - | - | - | - | - |
|
| 887 |
+
| 1.5429 | 1610 | 0.0041 | - | - | - | - | - |
|
| 888 |
+
| 1.5525 | 1620 | 0.0021 | - | - | - | - | - |
|
| 889 |
+
| 1.5621 | 1630 | 0.1113 | - | - | - | - | - |
|
| 890 |
+
| 1.5716 | 1640 | 0.0003 | - | - | - | - | - |
|
| 891 |
+
| 1.5812 | 1650 | 0.0003 | - | - | - | - | - |
|
| 892 |
+
| 1.5908 | 1660 | 0.0018 | - | - | - | - | - |
|
| 893 |
+
| 1.6004 | 1670 | 0.0004 | - | - | - | - | - |
|
| 894 |
+
| 1.6100 | 1680 | 0.0003 | - | - | - | - | - |
|
| 895 |
+
| 1.6195 | 1690 | 0.0017 | - | - | - | - | - |
|
| 896 |
+
| 1.6291 | 1700 | 0.0023 | - | - | - | - | - |
|
| 897 |
+
| 1.6387 | 1710 | 0.0167 | - | - | - | - | - |
|
| 898 |
+
| 1.6483 | 1720 | 0.0023 | - | - | - | - | - |
|
| 899 |
+
| 1.6579 | 1730 | 0.0095 | - | - | - | - | - |
|
| 900 |
+
| 1.6675 | 1740 | 0.0005 | - | - | - | - | - |
|
| 901 |
+
| 1.6770 | 1750 | 0.0014 | - | - | - | - | - |
|
| 902 |
+
| 1.6866 | 1760 | 0.0007 | - | - | - | - | - |
|
| 903 |
+
| 1.6962 | 1770 | 0.0014 | - | - | - | - | - |
|
| 904 |
+
| 1.7058 | 1780 | 0.0 | - | - | - | - | - |
|
| 905 |
+
| 1.7154 | 1790 | 0.0016 | - | - | - | - | - |
|
| 906 |
+
| 1.7250 | 1800 | 0.0004 | - | - | - | - | - |
|
| 907 |
+
| 1.7345 | 1810 | 0.0007 | - | - | - | - | - |
|
| 908 |
+
| 1.7441 | 1820 | 0.3356 | - | - | - | - | - |
|
| 909 |
+
| 1.7537 | 1830 | 0.001 | - | - | - | - | - |
|
| 910 |
+
| 1.7633 | 1840 | 0.0436 | - | - | - | - | - |
|
| 911 |
+
| 1.7729 | 1850 | 0.0839 | - | - | - | - | - |
|
| 912 |
+
| 1.7825 | 1860 | 0.0019 | - | - | - | - | - |
|
| 913 |
+
| 1.7920 | 1870 | 0.0406 | - | - | - | - | - |
|
| 914 |
+
| 1.8016 | 1880 | 0.0496 | - | - | - | - | - |
|
| 915 |
+
| 1.8112 | 1890 | 0.0164 | - | - | - | - | - |
|
| 916 |
+
| 1.8208 | 1900 | 0.0118 | - | - | - | - | - |
|
| 917 |
+
| 1.8304 | 1910 | 0.001 | - | - | - | - | - |
|
| 918 |
+
| 1.8400 | 1920 | 0.0004 | - | - | - | - | - |
|
| 919 |
+
| 1.8495 | 1930 | 0.002 | - | - | - | - | - |
|
| 920 |
+
| 1.8591 | 1940 | 0.0051 | - | - | - | - | - |
|
| 921 |
+
| 1.8687 | 1950 | 0.0624 | - | - | - | - | - |
|
| 922 |
+
| 1.8783 | 1960 | 0.0033 | - | - | - | - | - |
|
| 923 |
+
| 1.8879 | 1970 | 0.0001 | - | - | - | - | - |
|
| 924 |
+
| 1.8975 | 1980 | 0.1594 | - | - | - | - | - |
|
| 925 |
+
| 1.9070 | 1990 | 0.007 | - | - | - | - | - |
|
| 926 |
+
| 1.9166 | 2000 | 0.0002 | - | - | - | - | - |
|
| 927 |
+
| 1.9262 | 2010 | 0.0012 | - | - | - | - | - |
|
| 928 |
+
| 1.9358 | 2020 | 0.0011 | - | - | - | - | - |
|
| 929 |
+
| 1.9454 | 2030 | 0.0264 | - | - | - | - | - |
|
| 930 |
+
| 1.9550 | 2040 | 0.0004 | - | - | - | - | - |
|
| 931 |
+
| 1.9645 | 2050 | 0.008 | - | - | - | - | - |
|
| 932 |
+
| 1.9741 | 2060 | 0.1025 | - | - | - | - | - |
|
| 933 |
+
| 1.9837 | 2070 | 0.0745 | - | - | - | - | - |
|
| 934 |
+
| 1.9933 | 2080 | 0.006 | - | - | - | - | - |
|
| 935 |
+
| 2.0 | 2087 | - | 0.1609 | 0.1644 | 0.1708 | 0.1499 | 0.1696 |
|
| 936 |
+
| 2.0029 | 2090 | 0.001 | - | - | - | - | - |
|
| 937 |
+
| 2.0125 | 2100 | 0.0004 | - | - | - | - | - |
|
| 938 |
+
| 2.0220 | 2110 | 0.0003 | - | - | - | - | - |
|
| 939 |
+
| 2.0316 | 2120 | 0.0001 | - | - | - | - | - |
|
| 940 |
+
| 2.0412 | 2130 | 0.0003 | - | - | - | - | - |
|
| 941 |
+
| 2.0508 | 2140 | 0.0002 | - | - | - | - | - |
|
| 942 |
+
| 2.0604 | 2150 | 0.0006 | - | - | - | - | - |
|
| 943 |
+
| 2.0700 | 2160 | 0.04 | - | - | - | - | - |
|
| 944 |
+
| 2.0795 | 2170 | 0.0055 | - | - | - | - | - |
|
| 945 |
+
| 2.0891 | 2180 | 0.1454 | - | - | - | - | - |
|
| 946 |
+
| 2.0987 | 2190 | 0.0029 | - | - | - | - | - |
|
| 947 |
+
| 2.1083 | 2200 | 0.0006 | - | - | - | - | - |
|
| 948 |
+
| 2.1179 | 2210 | 0.0001 | - | - | - | - | - |
|
| 949 |
+
| 2.1275 | 2220 | 0.0129 | - | - | - | - | - |
|
| 950 |
+
| 2.1370 | 2230 | 0.0001 | - | - | - | - | - |
|
| 951 |
+
| 2.1466 | 2240 | 0.0003 | - | - | - | - | - |
|
| 952 |
+
| 2.1562 | 2250 | 0.4145 | - | - | - | - | - |
|
| 953 |
+
| 2.1658 | 2260 | 0.0048 | - | - | - | - | - |
|
| 954 |
+
| 2.1754 | 2270 | 0.0706 | - | - | - | - | - |
|
| 955 |
+
| 2.1850 | 2280 | 0.0026 | - | - | - | - | - |
|
| 956 |
+
| 2.1945 | 2290 | 0.008 | - | - | - | - | - |
|
| 957 |
+
| 2.2041 | 2300 | 0.0051 | - | - | - | - | - |
|
| 958 |
+
| 2.2137 | 2310 | 0.0307 | - | - | - | - | - |
|
| 959 |
+
| 2.2233 | 2320 | 0.0017 | - | - | - | - | - |
|
| 960 |
+
| 2.2329 | 2330 | 0.0005 | - | - | - | - | - |
|
| 961 |
+
| 2.2425 | 2340 | 0.0001 | - | - | - | - | - |
|
| 962 |
+
| 2.2520 | 2350 | 0.0001 | - | - | - | - | - |
|
| 963 |
+
| 2.2616 | 2360 | 0.0001 | - | - | - | - | - |
|
| 964 |
+
| 2.2712 | 2370 | 0.0461 | - | - | - | - | - |
|
| 965 |
+
| 2.2808 | 2380 | 0.0001 | - | - | - | - | - |
|
| 966 |
+
| 2.2904 | 2390 | 0.0003 | - | - | - | - | - |
|
| 967 |
+
| 2.3000 | 2400 | 0.001 | - | - | - | - | - |
|
| 968 |
+
| 2.3095 | 2410 | 0.0002 | - | - | - | - | - |
|
| 969 |
+
| 2.3191 | 2420 | 0.1568 | - | - | - | - | - |
|
| 970 |
+
| 2.3287 | 2430 | 0.0001 | - | - | - | - | - |
|
| 971 |
+
| 2.3383 | 2440 | 0.0005 | - | - | - | - | - |
|
| 972 |
+
| 2.3479 | 2450 | 0.0072 | - | - | - | - | - |
|
| 973 |
+
| 2.3575 | 2460 | 0.014 | - | - | - | - | - |
|
| 974 |
+
| 2.3670 | 2470 | 0.0003 | - | - | - | - | - |
|
| 975 |
+
| 2.3766 | 2480 | 0.0 | - | - | - | - | - |
|
| 976 |
+
| 2.3862 | 2490 | 0.0001 | - | - | - | - | - |
|
| 977 |
+
| 2.3958 | 2500 | 0.0008 | - | - | - | - | - |
|
| 978 |
+
| 2.4054 | 2510 | 0.0 | - | - | - | - | - |
|
| 979 |
+
| 2.4149 | 2520 | 0.0002 | - | - | - | - | - |
|
| 980 |
+
| 2.4245 | 2530 | 0.061 | - | - | - | - | - |
|
| 981 |
+
| 2.4341 | 2540 | 0.0005 | - | - | - | - | - |
|
| 982 |
+
| 2.4437 | 2550 | 0.0 | - | - | - | - | - |
|
| 983 |
+
| 2.4533 | 2560 | 0.0003 | - | - | - | - | - |
|
| 984 |
+
| 2.4629 | 2570 | 0.0095 | - | - | - | - | - |
|
| 985 |
+
| 2.4724 | 2580 | 0.0002 | - | - | - | - | - |
|
| 986 |
+
| 2.4820 | 2590 | 0.0 | - | - | - | - | - |
|
| 987 |
+
| 2.4916 | 2600 | 0.0003 | - | - | - | - | - |
|
| 988 |
+
| 2.5012 | 2610 | 0.0002 | - | - | - | - | - |
|
| 989 |
+
| 2.5108 | 2620 | 0.0035 | - | - | - | - | - |
|
| 990 |
+
| 2.5204 | 2630 | 0.0001 | - | - | - | - | - |
|
| 991 |
+
| 2.5299 | 2640 | 0.0 | - | - | - | - | - |
|
| 992 |
+
| 2.5395 | 2650 | 0.0017 | - | - | - | - | - |
|
| 993 |
+
| 2.5491 | 2660 | 0.0 | - | - | - | - | - |
|
| 994 |
+
| 2.5587 | 2670 | 0.0066 | - | - | - | - | - |
|
| 995 |
+
| 2.5683 | 2680 | 0.0004 | - | - | - | - | - |
|
| 996 |
+
| 2.5779 | 2690 | 0.0001 | - | - | - | - | - |
|
| 997 |
+
| 2.5874 | 2700 | 0.0 | - | - | - | - | - |
|
| 998 |
+
| 2.5970 | 2710 | 0.0 | - | - | - | - | - |
|
| 999 |
+
| 2.6066 | 2720 | 0.131 | - | - | - | - | - |
|
| 1000 |
+
| 2.6162 | 2730 | 0.0001 | - | - | - | - | - |
|
| 1001 |
+
| 2.6258 | 2740 | 0.0001 | - | - | - | - | - |
|
| 1002 |
+
| 2.6354 | 2750 | 0.0001 | - | - | - | - | - |
|
| 1003 |
+
| 2.6449 | 2760 | 0.0 | - | - | - | - | - |
|
| 1004 |
+
| 2.6545 | 2770 | 0.0003 | - | - | - | - | - |
|
| 1005 |
+
| 2.6641 | 2780 | 0.0095 | - | - | - | - | - |
|
| 1006 |
+
| 2.6737 | 2790 | 0.0 | - | - | - | - | - |
|
| 1007 |
+
| 2.6833 | 2800 | 0.0003 | - | - | - | - | - |
|
| 1008 |
+
| 2.6929 | 2810 | 0.0001 | - | - | - | - | - |
|
| 1009 |
+
| 2.7024 | 2820 | 0.0002 | - | - | - | - | - |
|
| 1010 |
+
| 2.7120 | 2830 | 0.0007 | - | - | - | - | - |
|
| 1011 |
+
| 2.7216 | 2840 | 0.0008 | - | - | - | - | - |
|
| 1012 |
+
| 2.7312 | 2850 | 0.0 | - | - | - | - | - |
|
| 1013 |
+
| 2.7408 | 2860 | 0.0002 | - | - | - | - | - |
|
| 1014 |
+
| 2.7504 | 2870 | 0.0003 | - | - | - | - | - |
|
| 1015 |
+
| 2.7599 | 2880 | 0.0062 | - | - | - | - | - |
|
| 1016 |
+
| 2.7695 | 2890 | 0.0415 | - | - | - | - | - |
|
| 1017 |
+
| 2.7791 | 2900 | 0.0002 | - | - | - | - | - |
|
| 1018 |
+
| 2.7887 | 2910 | 0.0024 | - | - | - | - | - |
|
| 1019 |
+
| 2.7983 | 2920 | 0.0022 | - | - | - | - | - |
|
| 1020 |
+
| 2.8079 | 2930 | 0.0014 | - | - | - | - | - |
|
| 1021 |
+
| 2.8174 | 2940 | 0.1301 | - | - | - | - | - |
|
| 1022 |
+
| 2.8270 | 2950 | 0.0 | - | - | - | - | - |
|
| 1023 |
+
| 2.8366 | 2960 | 0.0 | - | - | - | - | - |
|
| 1024 |
+
| 2.8462 | 2970 | 0.0 | - | - | - | - | - |
|
| 1025 |
+
| 2.8558 | 2980 | 0.0006 | - | - | - | - | - |
|
| 1026 |
+
| 2.8654 | 2990 | 0.0 | - | - | - | - | - |
|
| 1027 |
+
| 2.8749 | 3000 | 0.0235 | - | - | - | - | - |
|
| 1028 |
+
| 2.8845 | 3010 | 0.0001 | - | - | - | - | - |
|
| 1029 |
+
| 2.8941 | 3020 | 0.0285 | - | - | - | - | - |
|
| 1030 |
+
| 2.9037 | 3030 | 0.0 | - | - | - | - | - |
|
| 1031 |
+
| 2.9133 | 3040 | 0.0002 | - | - | - | - | - |
|
| 1032 |
+
| 2.9229 | 3050 | 0.0 | - | - | - | - | - |
|
| 1033 |
+
| 2.9324 | 3060 | 0.0005 | - | - | - | - | - |
|
| 1034 |
+
| 2.9420 | 3070 | 0.0001 | - | - | - | - | - |
|
| 1035 |
+
| 2.9516 | 3080 | 0.0011 | - | - | - | - | - |
|
| 1036 |
+
| 2.9612 | 3090 | 0.0 | - | - | - | - | - |
|
| 1037 |
+
| 2.9708 | 3100 | 0.0001 | - | - | - | - | - |
|
| 1038 |
+
| 2.9804 | 3110 | 0.0046 | - | - | - | - | - |
|
| 1039 |
+
| 2.9899 | 3120 | 0.0001 | - | - | - | - | - |
|
| 1040 |
+
| **2.9995** | **3130** | **0.0005** | **0.1622** | **0.1647** | **0.1635** | **0.1564** | **0.1617** |
|
| 1041 |
+
| 3.0091 | 3140 | 0.0 | - | - | - | - | - |
|
| 1042 |
+
| 3.0187 | 3150 | 0.0 | - | - | - | - | - |
|
| 1043 |
+
| 3.0283 | 3160 | 0.0 | - | - | - | - | - |
|
| 1044 |
+
| 3.0379 | 3170 | 0.0002 | - | - | - | - | - |
|
| 1045 |
+
| 3.0474 | 3180 | 0.0004 | - | - | - | - | - |
|
| 1046 |
+
| 3.0570 | 3190 | 0.1022 | - | - | - | - | - |
|
| 1047 |
+
| 3.0666 | 3200 | 0.0012 | - | - | - | - | - |
|
| 1048 |
+
| 3.0762 | 3210 | 0.0001 | - | - | - | - | - |
|
| 1049 |
+
| 3.0858 | 3220 | 0.0677 | - | - | - | - | - |
|
| 1050 |
+
| 3.0954 | 3230 | 0.0 | - | - | - | - | - |
|
| 1051 |
+
| 3.1049 | 3240 | 0.0002 | - | - | - | - | - |
|
| 1052 |
+
| 3.1145 | 3250 | 0.0001 | - | - | - | - | - |
|
| 1053 |
+
| 3.1241 | 3260 | 0.0005 | - | - | - | - | - |
|
| 1054 |
+
| 3.1337 | 3270 | 0.0002 | - | - | - | - | - |
|
| 1055 |
+
| 3.1433 | 3280 | 0.0 | - | - | - | - | - |
|
| 1056 |
+
| 3.1529 | 3290 | 0.0021 | - | - | - | - | - |
|
| 1057 |
+
| 3.1624 | 3300 | 0.0001 | - | - | - | - | - |
|
| 1058 |
+
| 3.1720 | 3310 | 0.0077 | - | - | - | - | - |
|
| 1059 |
+
| 3.1816 | 3320 | 0.0001 | - | - | - | - | - |
|
| 1060 |
+
| 3.1912 | 3330 | 0.1324 | - | - | - | - | - |
|
| 1061 |
+
| 3.2008 | 3340 | 0.0 | - | - | - | - | - |
|
| 1062 |
+
| 3.2103 | 3350 | 0.1278 | - | - | - | - | - |
|
| 1063 |
+
| 3.2199 | 3360 | 0.0001 | - | - | - | - | - |
|
| 1064 |
+
| 3.2295 | 3370 | 0.0 | - | - | - | - | - |
|
| 1065 |
+
| 3.2391 | 3380 | 0.0001 | - | - | - | - | - |
|
| 1066 |
+
| 3.2487 | 3390 | 0.0001 | - | - | - | - | - |
|
| 1067 |
+
| 3.2583 | 3400 | 0.0 | - | - | - | - | - |
|
| 1068 |
+
| 3.2678 | 3410 | 0.0001 | - | - | - | - | - |
|
| 1069 |
+
| 3.2774 | 3420 | 0.0 | - | - | - | - | - |
|
| 1070 |
+
| 3.2870 | 3430 | 0.0001 | - | - | - | - | - |
|
| 1071 |
+
| 3.2966 | 3440 | 0.0001 | - | - | - | - | - |
|
| 1072 |
+
| 3.3062 | 3450 | 0.0001 | - | - | - | - | - |
|
| 1073 |
+
| 3.3158 | 3460 | 0.0263 | - | - | - | - | - |
|
| 1074 |
+
| 3.3253 | 3470 | 0.0001 | - | - | - | - | - |
|
| 1075 |
+
| 3.3349 | 3480 | 0.0002 | - | - | - | - | - |
|
| 1076 |
+
| 3.3445 | 3490 | 0.0003 | - | - | - | - | - |
|
| 1077 |
+
| 3.3541 | 3500 | 0.0 | - | - | - | - | - |
|
| 1078 |
+
| 3.3637 | 3510 | 0.0 | - | - | - | - | - |
|
| 1079 |
+
| 3.3733 | 3520 | 0.0 | - | - | - | - | - |
|
| 1080 |
+
| 3.3828 | 3530 | 0.0002 | - | - | - | - | - |
|
| 1081 |
+
| 3.3924 | 3540 | 0.0001 | - | - | - | - | - |
|
| 1082 |
+
| 3.4020 | 3550 | 0.0 | - | - | - | - | - |
|
| 1083 |
+
| 3.4116 | 3560 | 0.0001 | - | - | - | - | - |
|
| 1084 |
+
| 3.4212 | 3570 | 0.0001 | - | - | - | - | - |
|
| 1085 |
+
| 3.4308 | 3580 | 0.0122 | - | - | - | - | - |
|
| 1086 |
+
| 3.4403 | 3590 | 0.0 | - | - | - | - | - |
|
| 1087 |
+
| 3.4499 | 3600 | 0.0001 | - | - | - | - | - |
|
| 1088 |
+
| 3.4595 | 3610 | 0.0003 | - | - | - | - | - |
|
| 1089 |
+
| 3.4691 | 3620 | 0.0 | - | - | - | - | - |
|
| 1090 |
+
| 3.4787 | 3630 | 0.0 | - | - | - | - | - |
|
| 1091 |
+
| 3.4883 | 3640 | 0.0001 | - | - | - | - | - |
|
| 1092 |
+
| 3.4978 | 3650 | 0.0 | - | - | - | - | - |
|
| 1093 |
+
| 3.5074 | 3660 | 0.0002 | - | - | - | - | - |
|
| 1094 |
+
| 3.5170 | 3670 | 0.0004 | - | - | - | - | - |
|
| 1095 |
+
| 3.5266 | 3680 | 0.0003 | - | - | - | - | - |
|
| 1096 |
+
| 3.5362 | 3690 | 0.0004 | - | - | - | - | - |
|
| 1097 |
+
| 3.5458 | 3700 | 0.0 | - | - | - | - | - |
|
| 1098 |
+
| 3.5553 | 3710 | 0.0001 | - | - | - | - | - |
|
| 1099 |
+
| 3.5649 | 3720 | 0.0001 | - | - | - | - | - |
|
| 1100 |
+
| 3.5745 | 3730 | 0.0 | - | - | - | - | - |
|
| 1101 |
+
| 3.5841 | 3740 | 0.0001 | - | - | - | - | - |
|
| 1102 |
+
| 3.5937 | 3750 | 0.0003 | - | - | - | - | - |
|
| 1103 |
+
| 3.6033 | 3760 | 0.0 | - | - | - | - | - |
|
| 1104 |
+
| 3.6128 | 3770 | 0.0002 | - | - | - | - | - |
|
| 1105 |
+
| 3.6224 | 3780 | 0.0 | - | - | - | - | - |
|
| 1106 |
+
| 3.6320 | 3790 | 0.0 | - | - | - | - | - |
|
| 1107 |
+
| 3.6416 | 3800 | 0.0 | - | - | - | - | - |
|
| 1108 |
+
| 3.6512 | 3810 | 0.0 | - | - | - | - | - |
|
| 1109 |
+
| 3.6608 | 3820 | 0.0 | - | - | - | - | - |
|
| 1110 |
+
| 3.6703 | 3830 | 0.0 | - | - | - | - | - |
|
| 1111 |
+
| 3.6799 | 3840 | 0.0001 | - | - | - | - | - |
|
| 1112 |
+
| 3.6895 | 3850 | 0.0001 | - | - | - | - | - |
|
| 1113 |
+
| 3.6991 | 3860 | 0.0002 | - | - | - | - | - |
|
| 1114 |
+
| 3.7087 | 3870 | 0.0 | - | - | - | - | - |
|
| 1115 |
+
| 3.7183 | 3880 | 0.0001 | - | - | - | - | - |
|
| 1116 |
+
| 3.7278 | 3890 | 0.0002 | - | - | - | - | - |
|
| 1117 |
+
| 3.7374 | 3900 | 0.0001 | - | - | - | - | - |
|
| 1118 |
+
| 3.7470 | 3910 | 0.0003 | - | - | - | - | - |
|
| 1119 |
+
| 3.7566 | 3920 | 0.0003 | - | - | - | - | - |
|
| 1120 |
+
| 3.7662 | 3930 | 0.0021 | - | - | - | - | - |
|
| 1121 |
+
| 3.7758 | 3940 | 0.0002 | - | - | - | - | - |
|
| 1122 |
+
| 3.7853 | 3950 | 0.0001 | - | - | - | - | - |
|
| 1123 |
+
| 3.7949 | 3960 | 0.0001 | - | - | - | - | - |
|
| 1124 |
+
| 3.8045 | 3970 | 0.0001 | - | - | - | - | - |
|
| 1125 |
+
| 3.8141 | 3980 | 0.0002 | - | - | - | - | - |
|
| 1126 |
+
| 3.8237 | 3990 | 0.0001 | - | - | - | - | - |
|
| 1127 |
+
| 3.8333 | 4000 | 0.0001 | - | - | - | - | - |
|
| 1128 |
+
| 3.8428 | 4010 | 0.0001 | - | - | - | - | - |
|
| 1129 |
+
| 3.8524 | 4020 | 0.0001 | - | - | - | - | - |
|
| 1130 |
+
| 3.8620 | 4030 | 0.0 | - | - | - | - | - |
|
| 1131 |
+
| 3.8716 | 4040 | 0.0003 | - | - | - | - | - |
|
| 1132 |
+
| 3.8812 | 4050 | 0.0 | - | - | - | - | - |
|
| 1133 |
+
| 3.8908 | 4060 | 0.002 | - | - | - | - | - |
|
| 1134 |
+
| 3.9003 | 4070 | 0.0 | - | - | - | - | - |
|
| 1135 |
+
| 3.9099 | 4080 | 0.0 | - | - | - | - | - |
|
| 1136 |
+
| 3.9195 | 4090 | 0.0001 | - | - | - | - | - |
|
| 1137 |
+
| 3.9291 | 4100 | 0.0 | - | - | - | - | - |
|
| 1138 |
+
| 3.9387 | 4110 | 0.0 | - | - | - | - | - |
|
| 1139 |
+
| 3.9483 | 4120 | 0.0 | - | - | - | - | - |
|
| 1140 |
+
| 3.9578 | 4130 | 0.0 | - | - | - | - | - |
|
| 1141 |
+
| 3.9674 | 4140 | 0.0 | - | - | - | - | - |
|
| 1142 |
+
| 3.9770 | 4150 | 0.0 | - | - | - | - | - |
|
| 1143 |
+
| 3.9866 | 4160 | 0.0004 | - | - | - | - | - |
|
| 1144 |
+
| 3.9962 | 4170 | 0.0 | - | - | - | - | - |
|
| 1145 |
+
| 3.9981 | 4172 | - | 0.1592 | 0.1658 | 0.1660 | 0.1580 | 0.1671 |
|
| 1146 |
+
|
| 1147 |
+
* The bold row denotes the saved checkpoint.
|
| 1148 |
+
</details>
|
| 1149 |
+
|
| 1150 |
+
### Framework Versions
|
| 1151 |
+
- Python: 3.10.12
|
| 1152 |
+
- Sentence Transformers: 3.0.1
|
| 1153 |
+
- Transformers: 4.42.4
|
| 1154 |
+
- PyTorch: 2.3.1+cu121
|
| 1155 |
+
- Accelerate: 0.34.0.dev0
|
| 1156 |
+
- Datasets: 2.21.0
|
| 1157 |
+
- Tokenizers: 0.19.1
|
| 1158 |
+
|
| 1159 |
+
## Citation
|
| 1160 |
+
|
| 1161 |
+
### BibTeX
|
| 1162 |
+
|
| 1163 |
+
#### Sentence Transformers
|
| 1164 |
+
```bibtex
|
| 1165 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 1166 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 1167 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 1168 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 1169 |
+
month = "11",
|
| 1170 |
+
year = "2019",
|
| 1171 |
+
publisher = "Association for Computational Linguistics",
|
| 1172 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 1173 |
+
}
|
| 1174 |
+
```
|
| 1175 |
+
|
| 1176 |
+
#### MatryoshkaLoss
|
| 1177 |
+
```bibtex
|
| 1178 |
+
@misc{kusupati2024matryoshka,
|
| 1179 |
+
title={Matryoshka Representation Learning},
|
| 1180 |
+
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
|
| 1181 |
+
year={2024},
|
| 1182 |
+
eprint={2205.13147},
|
| 1183 |
+
archivePrefix={arXiv},
|
| 1184 |
+
primaryClass={cs.LG}
|
| 1185 |
+
}
|
| 1186 |
+
```
|
| 1187 |
+
|
| 1188 |
+
#### MultipleNegativesRankingLoss
|
| 1189 |
+
```bibtex
|
| 1190 |
+
@misc{henderson2017efficient,
|
| 1191 |
+
title={Efficient Natural Language Response Suggestion for Smart Reply},
|
| 1192 |
+
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
|
| 1193 |
+
year={2017},
|
| 1194 |
+
eprint={1705.00652},
|
| 1195 |
+
archivePrefix={arXiv},
|
| 1196 |
+
primaryClass={cs.CL}
|
| 1197 |
+
}
|
| 1198 |
+
```
|
| 1199 |
+
|
| 1200 |
+
<!--
|
| 1201 |
+
## Glossary
|
| 1202 |
+
|
| 1203 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 1204 |
+
-->
|
| 1205 |
+
|
| 1206 |
+
<!--
|
| 1207 |
+
## Model Card Authors
|
| 1208 |
+
|
| 1209 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 1210 |
+
-->
|
| 1211 |
+
|
| 1212 |
+
<!--
|
| 1213 |
+
## Model Card Contact
|
| 1214 |
+
|
| 1215 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 1216 |
+
-->
|
config.json
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "BAAI/bge-m3",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"XLMRobertaModel"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"bos_token_id": 0,
|
| 8 |
+
"classifier_dropout": null,
|
| 9 |
+
"eos_token_id": 2,
|
| 10 |
+
"hidden_act": "gelu",
|
| 11 |
+
"hidden_dropout_prob": 0.1,
|
| 12 |
+
"hidden_size": 1024,
|
| 13 |
+
"initializer_range": 0.02,
|
| 14 |
+
"intermediate_size": 4096,
|
| 15 |
+
"layer_norm_eps": 1e-05,
|
| 16 |
+
"max_position_embeddings": 8194,
|
| 17 |
+
"model_type": "xlm-roberta",
|
| 18 |
+
"num_attention_heads": 16,
|
| 19 |
+
"num_hidden_layers": 24,
|
| 20 |
+
"output_past": true,
|
| 21 |
+
"pad_token_id": 1,
|
| 22 |
+
"position_embedding_type": "absolute",
|
| 23 |
+
"torch_dtype": "float32",
|
| 24 |
+
"transformers_version": "4.42.4",
|
| 25 |
+
"type_vocab_size": 1,
|
| 26 |
+
"use_cache": true,
|
| 27 |
+
"vocab_size": 250002
|
| 28 |
+
}
|
config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"__version__": {
|
| 3 |
+
"sentence_transformers": "3.0.1",
|
| 4 |
+
"transformers": "4.42.4",
|
| 5 |
+
"pytorch": "2.3.1+cu121"
|
| 6 |
+
},
|
| 7 |
+
"prompts": {},
|
| 8 |
+
"default_prompt_name": null,
|
| 9 |
+
"similarity_fn_name": null
|
| 10 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b796a12ea0352a66b71c2ed1b859eb6c7ebd5cb4f2fcd3cd23eae415157ac2fa
|
| 3 |
+
size 2271064456
|
modules.json
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
},
|
| 14 |
+
{
|
| 15 |
+
"idx": 2,
|
| 16 |
+
"name": "2",
|
| 17 |
+
"path": "2_Normalize",
|
| 18 |
+
"type": "sentence_transformers.models.Normalize"
|
| 19 |
+
}
|
| 20 |
+
]
|
sentence_bert_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"max_seq_length": 8192,
|
| 3 |
+
"do_lower_case": false
|
| 4 |
+
}
|
sentencepiece.bpe.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
|
| 3 |
+
size 5069051
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<s>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"cls_token": {
|
| 10 |
+
"content": "<s>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"eos_token": {
|
| 17 |
+
"content": "</s>",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"mask_token": {
|
| 24 |
+
"content": "<mask>",
|
| 25 |
+
"lstrip": true,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
},
|
| 30 |
+
"pad_token": {
|
| 31 |
+
"content": "<pad>",
|
| 32 |
+
"lstrip": false,
|
| 33 |
+
"normalized": false,
|
| 34 |
+
"rstrip": false,
|
| 35 |
+
"single_word": false
|
| 36 |
+
},
|
| 37 |
+
"sep_token": {
|
| 38 |
+
"content": "</s>",
|
| 39 |
+
"lstrip": false,
|
| 40 |
+
"normalized": false,
|
| 41 |
+
"rstrip": false,
|
| 42 |
+
"single_word": false
|
| 43 |
+
},
|
| 44 |
+
"unk_token": {
|
| 45 |
+
"content": "<unk>",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false
|
| 50 |
+
}
|
| 51 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e4f7e21bec3fb0044ca0bb2d50eb5d4d8c596273c422baef84466d2c73748b9c
|
| 3 |
+
size 17083053
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"0": {
|
| 4 |
+
"content": "<s>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"1": {
|
| 12 |
+
"content": "<pad>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"2": {
|
| 20 |
+
"content": "</s>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"3": {
|
| 28 |
+
"content": "<unk>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"250001": {
|
| 36 |
+
"content": "<mask>",
|
| 37 |
+
"lstrip": true,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
}
|
| 43 |
+
},
|
| 44 |
+
"bos_token": "<s>",
|
| 45 |
+
"clean_up_tokenization_spaces": true,
|
| 46 |
+
"cls_token": "<s>",
|
| 47 |
+
"eos_token": "</s>",
|
| 48 |
+
"mask_token": "<mask>",
|
| 49 |
+
"model_max_length": 8192,
|
| 50 |
+
"pad_token": "<pad>",
|
| 51 |
+
"sep_token": "</s>",
|
| 52 |
+
"sp_model_kwargs": {},
|
| 53 |
+
"tokenizer_class": "XLMRobertaTokenizer",
|
| 54 |
+
"unk_token": "<unk>"
|
| 55 |
+
}
|