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reasonwang
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embedding-qwen3-1.7b-embedding_unicode_shuf

Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
dense
Generated from Trainer
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
reasonwang commited on
Commit
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1 Parent(s): 69b7a3f

Add new SentenceTransformer model

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Files changed (17) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +1441 -0
  4. added_tokens.json +28 -0
  5. chat_template.jinja +85 -0
  6. config.json +60 -0
  7. config_sentence_transformers.json +14 -0
  8. merges.txt +0 -0
  9. model-00001-of-00002.safetensors +3 -0
  10. model-00002-of-00002.safetensors +3 -0
  11. model.safetensors.index.json +318 -0
  12. modules.json +14 -0
  13. sentence_bert_config.json +4 -0
  14. special_tokens_map.json +31 -0
  15. tokenizer.json +3 -0
  16. tokenizer_config.json +246 -0
  17. vocab.json +0 -0
.gitattributes CHANGED
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 2048,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": true,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,1441 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ tags:
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+ - sentence-transformers
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+ - sentence-similarity
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+ - feature-extraction
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+ - dense
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+ - generated_from_trainer
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+ - loss:CachedMultipleNegativesRankingLoss
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+ pipeline_tag: sentence-similarity
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+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy@1
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+ - cosine_accuracy@3
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+ - cosine_accuracy@5
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+ - cosine_accuracy@10
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+ - cosine_precision@1
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+ - cosine_precision@3
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+ - cosine_precision@5
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+ - cosine_precision@10
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+ - cosine_recall@1
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+ - cosine_recall@3
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+ - cosine_recall@5
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+ - cosine_recall@10
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+ - cosine_ndcg@10
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+ - cosine_ndcg@100
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+ - cosine_mrr@10
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+ - cosine_mrr@100
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+ - cosine_map@100
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+ model-index:
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+ - name: SentenceTransformer
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+ results:
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+ - task:
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+ type: information-retrieval
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+ name: Information Retrieval
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+ dataset:
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+ name: validation retrieval
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+ type: validation_retrieval
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+ metrics:
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+ - type: cosine_accuracy@1
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+ value: 0.57
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+ name: Cosine Accuracy@1
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+ - type: cosine_accuracy@3
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+ value: 0.78
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+ name: Cosine Accuracy@3
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+ - type: cosine_accuracy@5
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+ value: 0.85
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+ name: Cosine Accuracy@5
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+ - type: cosine_accuracy@10
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+ value: 0.91
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+ name: Cosine Accuracy@10
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+ - type: cosine_precision@1
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+ value: 0.57
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+ name: Cosine Precision@1
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+ - type: cosine_precision@3
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+ value: 0.47666666666666674
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+ name: Cosine Precision@3
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+ - type: cosine_precision@5
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+ value: 0.4120000000000001
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+ name: Cosine Precision@5
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+ - type: cosine_precision@10
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+ value: 0.31600000000000006
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+ name: Cosine Precision@10
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+ - type: cosine_recall@1
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+ value: 0.09586063814284759
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+ name: Cosine Recall@1
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+ - type: cosine_recall@3
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+ value: 0.1974064320530941
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+ name: Cosine Recall@3
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+ - type: cosine_recall@5
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+ value: 0.2626170559411045
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+ name: Cosine Recall@5
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+ - type: cosine_recall@10
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+ value: 0.3592334048227218
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+ name: Cosine Recall@10
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+ - type: cosine_ndcg@10
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+ value: 0.466909740701314
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+ name: Cosine Ndcg@10
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+ - type: cosine_ndcg@100
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+ value: 0.5134011865277004
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+ name: Cosine Ndcg@100
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+ - type: cosine_mrr@10
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+ value: 0.6836785714285715
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+ name: Cosine Mrr@10
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+ - type: cosine_mrr@100
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+ value: 0.6876848086206663
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+ name: Cosine Mrr@100
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+ - type: cosine_map@100
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+ value: 0.31888680716468715
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+ name: Cosine Map@100
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+ ---
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+
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+ # SentenceTransformer
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model trained on the generator dataset. It maps sentences & paragraphs to a 4096-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** Sentence Transformer
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+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
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+ - **Maximum Sequence Length:** 32768 tokens
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+ - **Output Dimensionality:** 4096 dimensions
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+ - **Similarity Function:** Cosine Similarity
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+ - **Training Dataset:**
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+ - generator
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
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+ (1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
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+ )
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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+ First install the Sentence Transformers library:
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+
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+ ```bash
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+ pip install -U sentence-transformers
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+ ```
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+
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+ Then you can load this model and run inference.
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+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("reasonwang/embedding-qwen3-1.7b-embedding_unicode_shuf")
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+ # Run inference
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+ sentences = [
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+ 'The weather is lovely today.',
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+ "It's so sunny outside!",
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+ 'He drove to the stadium.',
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+ ]
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+ embeddings = model.encode(sentences)
147
+ print(embeddings.shape)
148
+ # [3, 4096]
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+
150
+ # Get the similarity scores for the embeddings
151
+ similarities = model.similarity(embeddings, embeddings)
152
+ print(similarities)
153
+ # tensor([[1.0000, 0.9014, 0.7471],
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+ # [0.9014, 1.0000, 0.6621],
155
+ # [0.7471, 0.6621, 1.0000]])
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+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
161
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
164
+ -->
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+
166
+ <!--
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+ ### Downstream Usage (Sentence Transformers)
168
+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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+ </details>
174
+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
179
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
181
+
182
+ ## Evaluation
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+
184
+ ### Metrics
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+
186
+ #### Information Retrieval
187
+
188
+ * Dataset: `validation_retrieval`
189
+ * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
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+
191
+ | Metric | Value |
192
+ |:--------------------|:-----------|
193
+ | cosine_accuracy@1 | 0.57 |
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+ | cosine_accuracy@3 | 0.78 |
195
+ | cosine_accuracy@5 | 0.85 |
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+ | cosine_accuracy@10 | 0.91 |
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+ | cosine_precision@1 | 0.57 |
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+ | cosine_precision@3 | 0.4767 |
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+ | cosine_precision@5 | 0.412 |
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+ | cosine_precision@10 | 0.316 |
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+ | cosine_recall@1 | 0.0959 |
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+ | cosine_recall@3 | 0.1974 |
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+ | cosine_recall@5 | 0.2626 |
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+ | cosine_recall@10 | 0.3592 |
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+ | cosine_ndcg@10 | 0.4669 |
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+ | **cosine_ndcg@100** | **0.5134** |
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+ | cosine_mrr@10 | 0.6837 |
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+ | cosine_mrr@100 | 0.6877 |
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+ | cosine_map@100 | 0.3189 |
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+
211
+ <!--
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+ ## Bias, Risks and Limitations
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+
214
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
215
+ -->
216
+
217
+ <!--
218
+ ### Recommendations
219
+
220
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
221
+ -->
222
+
223
+ ## Training Details
224
+
225
+ ### Training Dataset
226
+
227
+ #### generator
228
+
229
+ * Dataset: generator
230
+ * Columns: <code>sentence1</code> and <code>sentence2</code>
231
+ * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
232
+ ```json
233
+ {
234
+ "scale": 20.0,
235
+ "similarity_fct": "cos_sim",
236
+ "mini_batch_size": 4,
237
+ "gather_across_devices": false
238
+ }
239
+ ```
240
+
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+ ### Training Hyperparameters
242
+ #### Non-Default Hyperparameters
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+
244
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 256
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+ - `learning_rate`: 2e-05
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+ - `max_steps`: 100000
248
+ - `log_level`: info
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+ - `bf16`: True
250
+ - `dataloader_num_workers`: 1
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': False, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
252
+
253
+ #### All Hyperparameters
254
+ <details><summary>Click to expand</summary>
255
+
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+ - `overwrite_output_dir`: False
257
+ - `do_predict`: False
258
+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
260
+ - `per_device_train_batch_size`: 256
261
+ - `per_device_eval_batch_size`: 8
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+ - `per_gpu_train_batch_size`: None
263
+ - `per_gpu_eval_batch_size`: None
264
+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
266
+ - `torch_empty_cache_steps`: None
267
+ - `learning_rate`: 2e-05
268
+ - `weight_decay`: 0.0
269
+ - `adam_beta1`: 0.9
270
+ - `adam_beta2`: 0.999
271
+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 3.0
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+ - `max_steps`: 100000
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+ - `lr_scheduler_type`: linear
276
+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.0
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+ - `warmup_steps`: 0
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+ - `log_level`: info
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
287
+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
290
+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `bf16`: True
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+ - `fp16`: False
295
+ - `fp16_opt_level`: O1
296
+ - `half_precision_backend`: auto
297
+ - `bf16_full_eval`: False
298
+ - `fp16_full_eval`: False
299
+ - `tf32`: None
300
+ - `local_rank`: 0
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+ - `ddp_backend`: None
302
+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
305
+ - `dataloader_drop_last`: True
306
+ - `dataloader_num_workers`: 1
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+ - `dataloader_prefetch_factor`: None
308
+ - `past_index`: -1
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+ - `disable_tqdm`: False
310
+ - `remove_unused_columns`: True
311
+ - `label_names`: None
312
+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
315
+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
317
+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': False, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
319
+ - `parallelism_config`: None
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch_fused
323
+ - `optim_args`: None
324
+ - `adafactor`: False
325
+ - `group_by_length`: False
326
+ - `length_column_name`: length
327
+ - `project`: huggingface
328
+ - `trackio_space_id`: trackio
329
+ - `ddp_find_unused_parameters`: None
330
+ - `ddp_bucket_cap_mb`: None
331
+ - `ddp_broadcast_buffers`: False
332
+ - `dataloader_pin_memory`: True
333
+ - `dataloader_persistent_workers`: False
334
+ - `skip_memory_metrics`: True
335
+ - `use_legacy_prediction_loop`: False
336
+ - `push_to_hub`: False
337
+ - `resume_from_checkpoint`: None
338
+ - `hub_model_id`: None
339
+ - `hub_strategy`: every_save
340
+ - `hub_private_repo`: None
341
+ - `hub_always_push`: False
342
+ - `hub_revision`: None
343
+ - `gradient_checkpointing`: False
344
+ - `gradient_checkpointing_kwargs`: None
345
+ - `include_inputs_for_metrics`: False
346
+ - `include_for_metrics`: []
347
+ - `eval_do_concat_batches`: True
348
+ - `fp16_backend`: auto
349
+ - `push_to_hub_model_id`: None
350
+ - `push_to_hub_organization`: None
351
+ - `mp_parameters`:
352
+ - `auto_find_batch_size`: False
353
+ - `full_determinism`: False
354
+ - `torchdynamo`: None
355
+ - `ray_scope`: last
356
+ - `ddp_timeout`: 1800
357
+ - `torch_compile`: False
358
+ - `torch_compile_backend`: None
359
+ - `torch_compile_mode`: None
360
+ - `include_tokens_per_second`: False
361
+ - `include_num_input_tokens_seen`: no
362
+ - `neftune_noise_alpha`: None
363
+ - `optim_target_modules`: None
364
+ - `batch_eval_metrics`: False
365
+ - `eval_on_start`: False
366
+ - `use_liger_kernel`: False
367
+ - `liger_kernel_config`: None
368
+ - `eval_use_gather_object`: False
369
+ - `average_tokens_across_devices`: True
370
+ - `prompts`: None
371
+ - `batch_sampler`: batch_sampler
372
+ - `multi_dataset_batch_sampler`: proportional
373
+ - `router_mapping`: {}
374
+ - `learning_rate_mapping`: {}
375
+
376
+ </details>
377
+
378
+ ### Training Logs
379
+ <details><summary>Click to expand</summary>
380
+
381
+ | Epoch | Step | Training Loss | validation_retrieval_cosine_ndcg@100 |
382
+ |:------:|:-----:|:-------------:|:------------------------------------:|
383
+ | 1e-05 | 1 | 5.2241 | - |
384
+ | 0.0001 | 10 | 3.8307 | - |
385
+ | 0.0002 | 20 | 2.8362 | - |
386
+ | 0.0003 | 30 | 2.5535 | - |
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+ | 0.0004 | 40 | 2.3874 | - |
388
+ | 0.0005 | 50 | 2.3478 | - |
389
+ | 0.0006 | 60 | 2.2947 | - |
390
+ | 0.0007 | 70 | 2.2224 | - |
391
+ | 0.0008 | 80 | 2.1957 | - |
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+ | 0.0009 | 90 | 2.1906 | - |
393
+ | 0.001 | 100 | 2.1638 | 0.4390 |
394
+ | 0.0011 | 110 | 2.141 | - |
395
+ | 0.0012 | 120 | 2.1424 | - |
396
+ | 0.0013 | 130 | 2.1075 | - |
397
+ | 0.0014 | 140 | 2.1 | - |
398
+ | 0.0015 | 150 | 2.087 | - |
399
+ | 0.0016 | 160 | 2.0809 | - |
400
+ | 0.0017 | 170 | 2.1087 | - |
401
+ | 0.0018 | 180 | 2.0668 | - |
402
+ | 0.0019 | 190 | 2.057 | - |
403
+ | 0.002 | 200 | 2.0647 | 0.4512 |
404
+ | 0.0021 | 210 | 2.0259 | - |
405
+ | 0.0022 | 220 | 2.0266 | - |
406
+ | 0.0023 | 230 | 2.0173 | - |
407
+ | 0.0024 | 240 | 2.0347 | - |
408
+ | 0.0025 | 250 | 2.0364 | - |
409
+ | 0.0026 | 260 | 1.9999 | - |
410
+ | 0.0027 | 270 | 2.0162 | - |
411
+ | 0.0028 | 280 | 1.9844 | - |
412
+ | 0.0029 | 290 | 1.9897 | - |
413
+ | 0.003 | 300 | 1.9756 | 0.4665 |
414
+ | 0.0031 | 310 | 1.9482 | - |
415
+ | 0.0032 | 320 | 1.97 | - |
416
+ | 0.0033 | 330 | 1.9571 | - |
417
+ | 0.0034 | 340 | 1.9638 | - |
418
+ | 0.0035 | 350 | 1.9598 | - |
419
+ | 0.0036 | 360 | 1.9482 | - |
420
+ | 0.0037 | 370 | 1.9507 | - |
421
+ | 0.0038 | 380 | 1.9339 | - |
422
+ | 0.0039 | 390 | 1.9431 | - |
423
+ | 0.004 | 400 | 1.9562 | 0.4728 |
424
+ | 0.0041 | 410 | 1.9217 | - |
425
+ | 0.0042 | 420 | 1.9493 | - |
426
+ | 0.0043 | 430 | 1.9322 | - |
427
+ | 0.0044 | 440 | 1.9046 | - |
428
+ | 0.0045 | 450 | 1.9142 | - |
429
+ | 0.0046 | 460 | 1.9117 | - |
430
+ | 0.0047 | 470 | 1.9063 | - |
431
+ | 0.0048 | 480 | 1.8879 | - |
432
+ | 0.0049 | 490 | 1.8921 | - |
433
+ | 0.005 | 500 | 1.9033 | 0.4813 |
434
+ | 0.0051 | 510 | 1.8904 | - |
435
+ | 0.0052 | 520 | 1.8941 | - |
436
+ | 0.0053 | 530 | 1.8901 | - |
437
+ | 0.0054 | 540 | 1.88 | - |
438
+ | 0.0055 | 550 | 1.8974 | - |
439
+ | 0.0056 | 560 | 1.8875 | - |
440
+ | 0.0057 | 570 | 1.8962 | - |
441
+ | 0.0058 | 580 | 1.9163 | - |
442
+ | 0.0059 | 590 | 1.8463 | - |
443
+ | 0.006 | 600 | 1.8859 | 0.4808 |
444
+ | 0.0061 | 610 | 1.8772 | - |
445
+ | 0.0062 | 620 | 1.8664 | - |
446
+ | 0.0063 | 630 | 1.8553 | - |
447
+ | 0.0064 | 640 | 1.8674 | - |
448
+ | 0.0065 | 650 | 1.8326 | - |
449
+ | 0.0066 | 660 | 1.8411 | - |
450
+ | 0.0067 | 670 | 1.8764 | - |
451
+ | 0.0068 | 680 | 1.8544 | - |
452
+ | 0.0069 | 690 | 1.8489 | - |
453
+ | 0.007 | 700 | 1.8488 | 0.4785 |
454
+ | 0.0071 | 710 | 1.8558 | - |
455
+ | 0.0072 | 720 | 1.8389 | - |
456
+ | 0.0073 | 730 | 1.8367 | - |
457
+ | 0.0074 | 740 | 1.8525 | - |
458
+ | 0.0075 | 750 | 1.8368 | - |
459
+ | 0.0076 | 760 | 1.8561 | - |
460
+ | 0.0077 | 770 | 1.8255 | - |
461
+ | 0.0078 | 780 | 1.8169 | - |
462
+ | 0.0079 | 790 | 1.8036 | - |
463
+ | 0.008 | 800 | 1.8187 | 0.4950 |
464
+ | 0.0081 | 810 | 1.835 | - |
465
+ | 0.0082 | 820 | 1.8459 | - |
466
+ | 0.0083 | 830 | 1.7894 | - |
467
+ | 0.0084 | 840 | 1.8323 | - |
468
+ | 0.0085 | 850 | 1.8184 | - |
469
+ | 0.0086 | 860 | 1.8148 | - |
470
+ | 0.0087 | 870 | 1.8335 | - |
471
+ | 0.0088 | 880 | 1.8051 | - |
472
+ | 0.0089 | 890 | 1.796 | - |
473
+ | 0.009 | 900 | 1.8298 | 0.4931 |
474
+ | 0.0091 | 910 | 1.8048 | - |
475
+ | 0.0092 | 920 | 1.7841 | - |
476
+ | 0.0093 | 930 | 1.8022 | - |
477
+ | 0.0094 | 940 | 1.7822 | - |
478
+ | 0.0095 | 950 | 1.8062 | - |
479
+ | 0.0096 | 960 | 1.8197 | - |
480
+ | 0.0097 | 970 | 1.7851 | - |
481
+ | 0.0098 | 980 | 1.7836 | - |
482
+ | 0.0099 | 990 | 1.7629 | - |
483
+ | 0.01 | 1000 | 1.8104 | 0.4907 |
484
+ | 0.0101 | 1010 | 1.7941 | - |
485
+ | 0.0102 | 1020 | 1.8084 | - |
486
+ | 0.0103 | 1030 | 1.801 | - |
487
+ | 0.0104 | 1040 | 1.782 | - |
488
+ | 0.0105 | 1050 | 1.8032 | - |
489
+ | 0.0106 | 1060 | 1.7978 | - |
490
+ | 0.0107 | 1070 | 1.758 | - |
491
+ | 0.0108 | 1080 | 1.7695 | - |
492
+ | 0.0109 | 1090 | 1.7961 | - |
493
+ | 0.011 | 1100 | 1.7667 | 0.4844 |
494
+ | 0.0111 | 1110 | 1.7644 | - |
495
+ | 0.0112 | 1120 | 1.7974 | - |
496
+ | 0.0113 | 1130 | 1.7672 | - |
497
+ | 0.0114 | 1140 | 1.7671 | - |
498
+ | 0.0115 | 1150 | 1.7945 | - |
499
+ | 0.0116 | 1160 | 1.7863 | - |
500
+ | 0.0117 | 1170 | 1.7613 | - |
501
+ | 0.0118 | 1180 | 1.7739 | - |
502
+ | 0.0119 | 1190 | 1.7759 | - |
503
+ | 0.012 | 1200 | 1.7736 | 0.4868 |
504
+ | 0.0121 | 1210 | 1.7507 | - |
505
+ | 0.0122 | 1220 | 1.7628 | - |
506
+ | 0.0123 | 1230 | 1.7723 | - |
507
+ | 0.0124 | 1240 | 1.7536 | - |
508
+ | 0.0125 | 1250 | 1.727 | - |
509
+ | 0.0126 | 1260 | 1.7445 | - |
510
+ | 0.0127 | 1270 | 1.7529 | - |
511
+ | 0.0128 | 1280 | 1.7623 | - |
512
+ | 0.0129 | 1290 | 1.7321 | - |
513
+ | 0.013 | 1300 | 1.7748 | 0.4892 |
514
+ | 0.0131 | 1310 | 1.7211 | - |
515
+ | 0.0132 | 1320 | 1.7745 | - |
516
+ | 0.0133 | 1330 | 1.7305 | - |
517
+ | 0.0134 | 1340 | 1.767 | - |
518
+ | 0.0135 | 1350 | 1.7551 | - |
519
+ | 0.0136 | 1360 | 1.7329 | - |
520
+ | 0.0137 | 1370 | 1.78 | - |
521
+ | 0.0138 | 1380 | 1.7495 | - |
522
+ | 0.0139 | 1390 | 1.724 | - |
523
+ | 0.014 | 1400 | 1.7436 | 0.4939 |
524
+ | 0.0141 | 1410 | 1.7756 | - |
525
+ | 0.0142 | 1420 | 1.7748 | - |
526
+ | 0.0143 | 1430 | 1.7508 | - |
527
+ | 0.0144 | 1440 | 1.7301 | - |
528
+ | 0.0145 | 1450 | 1.746 | - |
529
+ | 0.0146 | 1460 | 1.7387 | - |
530
+ | 0.0147 | 1470 | 1.7368 | - |
531
+ | 0.0148 | 1480 | 1.7422 | - |
532
+ | 0.0149 | 1490 | 1.7335 | - |
533
+ | 0.015 | 1500 | 1.7129 | 0.4923 |
534
+ | 0.0151 | 1510 | 1.731 | - |
535
+ | 0.0152 | 1520 | 1.7307 | - |
536
+ | 0.0153 | 1530 | 1.7322 | - |
537
+ | 0.0154 | 1540 | 1.7554 | - |
538
+ | 0.0155 | 1550 | 1.7125 | - |
539
+ | 0.0156 | 1560 | 1.7327 | - |
540
+ | 0.0157 | 1570 | 1.7223 | - |
541
+ | 0.0158 | 1580 | 1.7136 | - |
542
+ | 0.0159 | 1590 | 1.7267 | - |
543
+ | 0.016 | 1600 | 1.7625 | 0.4914 |
544
+ | 0.0161 | 1610 | 1.7267 | - |
545
+ | 0.0162 | 1620 | 1.7038 | - |
546
+ | 0.0163 | 1630 | 1.7063 | - |
547
+ | 0.0164 | 1640 | 1.7057 | - |
548
+ | 0.0165 | 1650 | 1.7284 | - |
549
+ | 0.0166 | 1660 | 1.6998 | - |
550
+ | 0.0167 | 1670 | 1.7115 | - |
551
+ | 0.0168 | 1680 | 1.7356 | - |
552
+ | 0.0169 | 1690 | 1.6929 | - |
553
+ | 0.017 | 1700 | 1.7292 | 0.4985 |
554
+ | 0.0171 | 1710 | 1.7109 | - |
555
+ | 0.0172 | 1720 | 1.7494 | - |
556
+ | 0.0173 | 1730 | 1.7181 | - |
557
+ | 0.0174 | 1740 | 1.712 | - |
558
+ | 0.0175 | 1750 | 1.708 | - |
559
+ | 0.0176 | 1760 | 1.7056 | - |
560
+ | 0.0177 | 1770 | 1.7039 | - |
561
+ | 0.0178 | 1780 | 1.6837 | - |
562
+ | 0.0179 | 1790 | 1.7071 | - |
563
+ | 0.018 | 1800 | 1.7121 | 0.4972 |
564
+ | 0.0181 | 1810 | 1.7147 | - |
565
+ | 0.0182 | 1820 | 1.7203 | - |
566
+ | 0.0183 | 1830 | 1.7023 | - |
567
+ | 0.0184 | 1840 | 1.7278 | - |
568
+ | 0.0185 | 1850 | 1.7129 | - |
569
+ | 0.0186 | 1860 | 1.7454 | - |
570
+ | 0.0187 | 1870 | 1.7011 | - |
571
+ | 0.0188 | 1880 | 1.6996 | - |
572
+ | 0.0189 | 1890 | 1.7046 | - |
573
+ | 0.019 | 1900 | 1.6877 | 0.4984 |
574
+ | 0.0191 | 1910 | 1.6962 | - |
575
+ | 0.0192 | 1920 | 1.7057 | - |
576
+ | 0.0193 | 1930 | 1.6968 | - |
577
+ | 0.0194 | 1940 | 1.6994 | - |
578
+ | 0.0195 | 1950 | 1.7087 | - |
579
+ | 0.0196 | 1960 | 1.6832 | - |
580
+ | 0.0197 | 1970 | 1.686 | - |
581
+ | 0.0198 | 1980 | 1.7101 | - |
582
+ | 0.0199 | 1990 | 1.7024 | - |
583
+ | 0.02 | 2000 | 1.6875 | 0.4987 |
584
+ | 0.0201 | 2010 | 1.6872 | - |
585
+ | 0.0202 | 2020 | 1.6918 | - |
586
+ | 0.0203 | 2030 | 1.6735 | - |
587
+ | 0.0204 | 2040 | 1.6869 | - |
588
+ | 0.0205 | 2050 | 1.7082 | - |
589
+ | 0.0206 | 2060 | 1.6991 | - |
590
+ | 0.0207 | 2070 | 1.6973 | - |
591
+ | 0.0208 | 2080 | 1.6754 | - |
592
+ | 0.0209 | 2090 | 1.6953 | - |
593
+ | 0.021 | 2100 | 1.7065 | 0.4954 |
594
+ | 0.0211 | 2110 | 1.6804 | - |
595
+ | 0.0212 | 2120 | 1.6705 | - |
596
+ | 0.0213 | 2130 | 1.673 | - |
597
+ | 0.0214 | 2140 | 1.6997 | - |
598
+ | 0.0215 | 2150 | 1.6774 | - |
599
+ | 0.0216 | 2160 | 1.7124 | - |
600
+ | 0.0217 | 2170 | 1.6749 | - |
601
+ | 0.0218 | 2180 | 1.6661 | - |
602
+ | 0.0219 | 2190 | 1.6782 | - |
603
+ | 0.022 | 2200 | 1.6742 | 0.4922 |
604
+ | 0.0221 | 2210 | 1.7204 | - |
605
+ | 0.0222 | 2220 | 1.7081 | - |
606
+ | 0.0223 | 2230 | 1.681 | - |
607
+ | 0.0224 | 2240 | 1.6775 | - |
608
+ | 0.0225 | 2250 | 1.665 | - |
609
+ | 0.0226 | 2260 | 1.6992 | - |
610
+ | 0.0227 | 2270 | 1.6531 | - |
611
+ | 0.0228 | 2280 | 1.6656 | - |
612
+ | 0.0229 | 2290 | 1.6717 | - |
613
+ | 0.023 | 2300 | 1.6922 | 0.4982 |
614
+ | 0.0231 | 2310 | 1.6765 | - |
615
+ | 0.0232 | 2320 | 1.6687 | - |
616
+ | 0.0233 | 2330 | 1.6682 | - |
617
+ | 0.0234 | 2340 | 1.6734 | - |
618
+ | 0.0235 | 2350 | 1.6735 | - |
619
+ | 0.0236 | 2360 | 1.6935 | - |
620
+ | 0.0237 | 2370 | 1.6753 | - |
621
+ | 0.0238 | 2380 | 1.6571 | - |
622
+ | 0.0239 | 2390 | 1.6834 | - |
623
+ | 0.024 | 2400 | 1.6964 | 0.4986 |
624
+ | 0.0241 | 2410 | 1.6722 | - |
625
+ | 0.0242 | 2420 | 1.655 | - |
626
+ | 0.0243 | 2430 | 1.6599 | - |
627
+ | 0.0244 | 2440 | 1.6362 | - |
628
+ | 0.0245 | 2450 | 1.6715 | - |
629
+ | 0.0246 | 2460 | 1.6889 | - |
630
+ | 0.0247 | 2470 | 1.6778 | - |
631
+ | 0.0248 | 2480 | 1.6746 | - |
632
+ | 0.0249 | 2490 | 1.6739 | - |
633
+ | 0.025 | 2500 | 1.6478 | 0.4960 |
634
+ | 0.0251 | 2510 | 1.6609 | - |
635
+ | 0.0252 | 2520 | 1.6575 | - |
636
+ | 0.0253 | 2530 | 1.6393 | - |
637
+ | 0.0254 | 2540 | 1.6665 | - |
638
+ | 0.0255 | 2550 | 1.637 | - |
639
+ | 0.0256 | 2560 | 1.6294 | - |
640
+ | 0.0257 | 2570 | 1.6586 | - |
641
+ | 0.0258 | 2580 | 1.6625 | - |
642
+ | 0.0259 | 2590 | 1.6465 | - |
643
+ | 0.026 | 2600 | 1.6576 | 0.4993 |
644
+ | 0.0261 | 2610 | 1.6683 | - |
645
+ | 0.0262 | 2620 | 1.6447 | - |
646
+ | 0.0263 | 2630 | 1.6647 | - |
647
+ | 0.0264 | 2640 | 1.641 | - |
648
+ | 0.0265 | 2650 | 1.6544 | - |
649
+ | 0.0266 | 2660 | 1.6366 | - |
650
+ | 0.0267 | 2670 | 1.6368 | - |
651
+ | 0.0268 | 2680 | 1.668 | - |
652
+ | 0.0269 | 2690 | 1.6355 | - |
653
+ | 0.027 | 2700 | 1.6621 | 0.5026 |
654
+ | 0.0271 | 2710 | 1.6472 | - |
655
+ | 0.0272 | 2720 | 1.6579 | - |
656
+ | 0.0273 | 2730 | 1.6631 | - |
657
+ | 0.0274 | 2740 | 1.6627 | - |
658
+ | 0.0275 | 2750 | 1.6485 | - |
659
+ | 0.0276 | 2760 | 1.655 | - |
660
+ | 0.0277 | 2770 | 1.656 | - |
661
+ | 0.0278 | 2780 | 1.6425 | - |
662
+ | 0.0279 | 2790 | 1.6207 | - |
663
+ | 0.028 | 2800 | 1.6438 | 0.5061 |
664
+ | 0.0281 | 2810 | 1.6466 | - |
665
+ | 0.0282 | 2820 | 1.625 | - |
666
+ | 0.0283 | 2830 | 1.6672 | - |
667
+ | 0.0284 | 2840 | 1.6154 | - |
668
+ | 0.0285 | 2850 | 1.6581 | - |
669
+ | 0.0286 | 2860 | 1.638 | - |
670
+ | 0.0287 | 2870 | 1.6252 | - |
671
+ | 0.0288 | 2880 | 1.6468 | - |
672
+ | 0.0289 | 2890 | 1.638 | - |
673
+ | 0.029 | 2900 | 1.67 | 0.4955 |
674
+ | 0.0291 | 2910 | 1.6236 | - |
675
+ | 0.0292 | 2920 | 1.6583 | - |
676
+ | 0.0293 | 2930 | 1.6596 | - |
677
+ | 0.0294 | 2940 | 1.6437 | - |
678
+ | 0.0295 | 2950 | 1.6362 | - |
679
+ | 0.0296 | 2960 | 1.6505 | - |
680
+ | 0.0297 | 2970 | 1.6299 | - |
681
+ | 0.0298 | 2980 | 1.6276 | - |
682
+ | 0.0299 | 2990 | 1.6274 | - |
683
+ | 0.03 | 3000 | 1.6666 | 0.5002 |
684
+ | 0.0301 | 3010 | 1.6358 | - |
685
+ | 0.0302 | 3020 | 1.6166 | - |
686
+ | 0.0303 | 3030 | 1.6491 | - |
687
+ | 0.0304 | 3040 | 1.6289 | - |
688
+ | 0.0305 | 3050 | 1.6544 | - |
689
+ | 0.0306 | 3060 | 1.6237 | - |
690
+ | 0.0307 | 3070 | 1.6131 | - |
691
+ | 0.0308 | 3080 | 1.6332 | - |
692
+ | 0.0309 | 3090 | 1.6182 | - |
693
+ | 0.031 | 3100 | 1.6344 | 0.5085 |
694
+ | 0.0311 | 3110 | 1.6217 | - |
695
+ | 0.0312 | 3120 | 1.6532 | - |
696
+ | 0.0313 | 3130 | 1.6315 | - |
697
+ | 0.0314 | 3140 | 1.6342 | - |
698
+ | 0.0315 | 3150 | 1.6281 | - |
699
+ | 0.0316 | 3160 | 1.6277 | - |
700
+ | 0.0317 | 3170 | 1.6527 | - |
701
+ | 0.0318 | 3180 | 1.6129 | - |
702
+ | 0.0319 | 3190 | 1.6247 | - |
703
+ | 0.032 | 3200 | 1.6439 | 0.5018 |
704
+ | 0.0321 | 3210 | 1.6422 | - |
705
+ | 0.0322 | 3220 | 1.6442 | - |
706
+ | 0.0323 | 3230 | 1.6632 | - |
707
+ | 0.0324 | 3240 | 1.6302 | - |
708
+ | 0.0325 | 3250 | 1.6162 | - |
709
+ | 0.0326 | 3260 | 1.6381 | - |
710
+ | 0.0327 | 3270 | 1.6137 | - |
711
+ | 0.0328 | 3280 | 1.6122 | - |
712
+ | 0.0329 | 3290 | 1.6224 | - |
713
+ | 0.033 | 3300 | 1.612 | 0.4993 |
714
+ | 0.0331 | 3310 | 1.6095 | - |
715
+ | 0.0332 | 3320 | 1.6206 | - |
716
+ | 0.0333 | 3330 | 1.6262 | - |
717
+ | 0.0334 | 3340 | 1.6052 | - |
718
+ | 0.0335 | 3350 | 1.6187 | - |
719
+ | 0.0336 | 3360 | 1.6145 | - |
720
+ | 0.0337 | 3370 | 1.6275 | - |
721
+ | 0.0338 | 3380 | 1.6093 | - |
722
+ | 0.0339 | 3390 | 1.6284 | - |
723
+ | 0.034 | 3400 | 1.6184 | 0.5079 |
724
+ | 0.0341 | 3410 | 1.6359 | - |
725
+ | 0.0342 | 3420 | 1.6208 | - |
726
+ | 0.0343 | 3430 | 1.6208 | - |
727
+ | 0.0344 | 3440 | 1.6341 | - |
728
+ | 0.0345 | 3450 | 1.6171 | - |
729
+ | 0.0346 | 3460 | 1.6122 | - |
730
+ | 0.0347 | 3470 | 1.6302 | - |
731
+ | 0.0348 | 3480 | 1.6214 | - |
732
+ | 0.0349 | 3490 | 1.6299 | - |
733
+ | 0.035 | 3500 | 1.6201 | 0.5005 |
734
+ | 0.0351 | 3510 | 1.6033 | - |
735
+ | 0.0352 | 3520 | 1.6202 | - |
736
+ | 0.0353 | 3530 | 1.6198 | - |
737
+ | 0.0354 | 3540 | 1.6207 | - |
738
+ | 0.0355 | 3550 | 1.6111 | - |
739
+ | 0.0356 | 3560 | 1.6196 | - |
740
+ | 0.0357 | 3570 | 1.6341 | - |
741
+ | 0.0358 | 3580 | 1.6086 | - |
742
+ | 0.0359 | 3590 | 1.6021 | - |
743
+ | 0.036 | 3600 | 1.6294 | 0.5161 |
744
+ | 0.0361 | 3610 | 1.6117 | - |
745
+ | 0.0362 | 3620 | 1.6368 | - |
746
+ | 0.0363 | 3630 | 1.6009 | - |
747
+ | 0.0364 | 3640 | 1.5983 | - |
748
+ | 0.0365 | 3650 | 1.6248 | - |
749
+ | 0.0366 | 3660 | 1.609 | - |
750
+ | 0.0367 | 3670 | 1.5975 | - |
751
+ | 0.0368 | 3680 | 1.6043 | - |
752
+ | 0.0369 | 3690 | 1.5989 | - |
753
+ | 0.037 | 3700 | 1.6164 | 0.5117 |
754
+ | 0.0371 | 3710 | 1.6283 | - |
755
+ | 0.0372 | 3720 | 1.5928 | - |
756
+ | 0.0373 | 3730 | 1.6104 | - |
757
+ | 0.0374 | 3740 | 1.6264 | - |
758
+ | 0.0375 | 3750 | 1.5989 | - |
759
+ | 0.0376 | 3760 | 1.5975 | - |
760
+ | 0.0377 | 3770 | 1.6011 | - |
761
+ | 0.0378 | 3780 | 1.6054 | - |
762
+ | 0.0379 | 3790 | 1.6129 | - |
763
+ | 0.038 | 3800 | 1.616 | 0.5119 |
764
+ | 0.0381 | 3810 | 1.618 | - |
765
+ | 0.0382 | 3820 | 1.6236 | - |
766
+ | 0.0383 | 3830 | 1.6032 | - |
767
+ | 0.0384 | 3840 | 1.6236 | - |
768
+ | 0.0385 | 3850 | 1.6003 | - |
769
+ | 0.0386 | 3860 | 1.6025 | - |
770
+ | 0.0387 | 3870 | 1.6034 | - |
771
+ | 0.0388 | 3880 | 1.599 | - |
772
+ | 0.0389 | 3890 | 1.6065 | - |
773
+ | 0.039 | 3900 | 1.6161 | 0.5097 |
774
+ | 0.0391 | 3910 | 1.6093 | - |
775
+ | 0.0392 | 3920 | 1.5912 | - |
776
+ | 0.0393 | 3930 | 1.5893 | - |
777
+ | 0.0394 | 3940 | 1.602 | - |
778
+ | 0.0395 | 3950 | 1.6023 | - |
779
+ | 0.0396 | 3960 | 1.6072 | - |
780
+ | 0.0397 | 3970 | 1.599 | - |
781
+ | 0.0398 | 3980 | 1.6083 | - |
782
+ | 0.0399 | 3990 | 1.5991 | - |
783
+ | 0.04 | 4000 | 1.6085 | 0.5089 |
784
+ | 0.0401 | 4010 | 1.5917 | - |
785
+ | 0.0402 | 4020 | 1.5934 | - |
786
+ | 0.0403 | 4030 | 1.5862 | - |
787
+ | 0.0404 | 4040 | 1.6041 | - |
788
+ | 0.0405 | 4050 | 1.6048 | - |
789
+ | 0.0406 | 4060 | 1.6145 | - |
790
+ | 0.0407 | 4070 | 1.5817 | - |
791
+ | 0.0408 | 4080 | 1.5848 | - |
792
+ | 0.0409 | 4090 | 1.6079 | - |
793
+ | 0.041 | 4100 | 1.5973 | 0.5101 |
794
+ | 0.0411 | 4110 | 1.6045 | - |
795
+ | 0.0412 | 4120 | 1.6083 | - |
796
+ | 0.0413 | 4130 | 1.5871 | - |
797
+ | 0.0414 | 4140 | 1.5891 | - |
798
+ | 0.0415 | 4150 | 1.5788 | - |
799
+ | 0.0416 | 4160 | 1.5859 | - |
800
+ | 0.0417 | 4170 | 1.6094 | - |
801
+ | 0.0418 | 4180 | 1.5684 | - |
802
+ | 0.0419 | 4190 | 1.5735 | - |
803
+ | 0.042 | 4200 | 1.5902 | 0.5126 |
804
+ | 0.0421 | 4210 | 1.578 | - |
805
+ | 0.0422 | 4220 | 1.5966 | - |
806
+ | 0.0423 | 4230 | 1.6026 | - |
807
+ | 0.0424 | 4240 | 1.6029 | - |
808
+ | 0.0425 | 4250 | 1.6013 | - |
809
+ | 0.0426 | 4260 | 1.5849 | - |
810
+ | 0.0427 | 4270 | 1.6059 | - |
811
+ | 0.0428 | 4280 | 1.6116 | - |
812
+ | 0.0429 | 4290 | 1.5928 | - |
813
+ | 0.043 | 4300 | 1.5911 | 0.5132 |
814
+ | 0.0431 | 4310 | 1.5873 | - |
815
+ | 0.0432 | 4320 | 1.611 | - |
816
+ | 0.0433 | 4330 | 1.5959 | - |
817
+ | 0.0434 | 4340 | 1.61 | - |
818
+ | 0.0435 | 4350 | 1.5948 | - |
819
+ | 0.0436 | 4360 | 1.5947 | - |
820
+ | 0.0437 | 4370 | 1.5969 | - |
821
+ | 0.0438 | 4380 | 1.5894 | - |
822
+ | 0.0439 | 4390 | 1.5905 | - |
823
+ | 0.044 | 4400 | 1.5826 | 0.5057 |
824
+ | 0.0441 | 4410 | 1.5852 | - |
825
+ | 0.0442 | 4420 | 1.6171 | - |
826
+ | 0.0443 | 4430 | 1.5904 | - |
827
+ | 0.0444 | 4440 | 1.5873 | - |
828
+ | 0.0445 | 4450 | 1.5963 | - |
829
+ | 0.0446 | 4460 | 1.6266 | - |
830
+ | 0.0447 | 4470 | 1.559 | - |
831
+ | 0.0448 | 4480 | 1.6168 | - |
832
+ | 0.0449 | 4490 | 1.599 | - |
833
+ | 0.045 | 4500 | 1.5974 | 0.5169 |
834
+ | 0.0451 | 4510 | 1.5921 | - |
835
+ | 0.0452 | 4520 | 1.5812 | - |
836
+ | 0.0453 | 4530 | 1.6036 | - |
837
+ | 0.0454 | 4540 | 1.5925 | - |
838
+ | 0.0455 | 4550 | 1.5967 | - |
839
+ | 0.0456 | 4560 | 1.5747 | - |
840
+ | 0.0457 | 4570 | 1.6012 | - |
841
+ | 0.0458 | 4580 | 1.6041 | - |
842
+ | 0.0459 | 4590 | 1.5917 | - |
843
+ | 0.046 | 4600 | 1.5615 | 0.5125 |
844
+ | 0.0461 | 4610 | 1.5794 | - |
845
+ | 0.0462 | 4620 | 1.5635 | - |
846
+ | 0.0463 | 4630 | 1.5968 | - |
847
+ | 0.0464 | 4640 | 1.6121 | - |
848
+ | 0.0465 | 4650 | 1.595 | - |
849
+ | 0.0466 | 4660 | 1.5875 | - |
850
+ | 0.0467 | 4670 | 1.5933 | - |
851
+ | 0.0468 | 4680 | 1.6045 | - |
852
+ | 0.0469 | 4690 | 1.5688 | - |
853
+ | 0.047 | 4700 | 1.5843 | 0.5133 |
854
+ | 0.0471 | 4710 | 1.5679 | - |
855
+ | 0.0472 | 4720 | 1.5736 | - |
856
+ | 0.0473 | 4730 | 1.5801 | - |
857
+ | 0.0474 | 4740 | 1.5753 | - |
858
+ | 0.0475 | 4750 | 1.5563 | - |
859
+ | 0.0476 | 4760 | 1.5565 | - |
860
+ | 0.0477 | 4770 | 1.5823 | - |
861
+ | 0.0478 | 4780 | 1.5866 | - |
862
+ | 0.0479 | 4790 | 1.5576 | - |
863
+ | 0.048 | 4800 | 1.5866 | 0.5147 |
864
+ | 0.0481 | 4810 | 1.5836 | - |
865
+ | 0.0482 | 4820 | 1.5993 | - |
866
+ | 0.0483 | 4830 | 1.5777 | - |
867
+ | 0.0484 | 4840 | 1.5642 | - |
868
+ | 0.0485 | 4850 | 1.5727 | - |
869
+ | 0.0486 | 4860 | 1.5621 | - |
870
+ | 0.0487 | 4870 | 1.5808 | - |
871
+ | 0.0488 | 4880 | 1.5604 | - |
872
+ | 0.0489 | 4890 | 1.5805 | - |
873
+ | 0.049 | 4900 | 1.5658 | 0.5227 |
874
+ | 0.0491 | 4910 | 1.5873 | - |
875
+ | 0.0492 | 4920 | 1.5984 | - |
876
+ | 0.0493 | 4930 | 1.5765 | - |
877
+ | 0.0494 | 4940 | 1.5659 | - |
878
+ | 0.0495 | 4950 | 1.5616 | - |
879
+ | 0.0496 | 4960 | 1.5851 | - |
880
+ | 0.0497 | 4970 | 1.5687 | - |
881
+ | 0.0498 | 4980 | 1.5758 | - |
882
+ | 0.0499 | 4990 | 1.588 | - |
883
+ | 0.05 | 5000 | 1.5564 | 0.5119 |
884
+ | 0.0501 | 5010 | 1.5673 | - |
885
+ | 0.0502 | 5020 | 1.5774 | - |
886
+ | 0.0503 | 5030 | 1.5763 | - |
887
+ | 0.0504 | 5040 | 1.572 | - |
888
+ | 0.0505 | 5050 | 1.5716 | - |
889
+ | 0.0506 | 5060 | 1.5634 | - |
890
+ | 0.0507 | 5070 | 1.5205 | - |
891
+ | 0.0508 | 5080 | 1.556 | - |
892
+ | 0.0509 | 5090 | 1.5651 | - |
893
+ | 0.051 | 5100 | 1.5684 | 0.5085 |
894
+ | 0.0511 | 5110 | 1.5629 | - |
895
+ | 0.0512 | 5120 | 1.5434 | - |
896
+ | 0.0513 | 5130 | 1.5796 | - |
897
+ | 0.0514 | 5140 | 1.574 | - |
898
+ | 0.0515 | 5150 | 1.5571 | - |
899
+ | 0.0516 | 5160 | 1.5433 | - |
900
+ | 0.0517 | 5170 | 1.5617 | - |
901
+ | 0.0518 | 5180 | 1.6022 | - |
902
+ | 0.0519 | 5190 | 1.5453 | - |
903
+ | 0.052 | 5200 | 1.5533 | 0.5091 |
904
+ | 0.0521 | 5210 | 1.5588 | - |
905
+ | 0.0522 | 5220 | 1.5756 | - |
906
+ | 0.0523 | 5230 | 1.5698 | - |
907
+ | 0.0524 | 5240 | 1.5916 | - |
908
+ | 0.0525 | 5250 | 1.5669 | - |
909
+ | 0.0526 | 5260 | 1.5595 | - |
910
+ | 0.0527 | 5270 | 1.5896 | - |
911
+ | 0.0528 | 5280 | 1.564 | - |
912
+ | 0.0529 | 5290 | 1.5819 | - |
913
+ | 0.053 | 5300 | 1.5674 | 0.5046 |
914
+ | 0.0531 | 5310 | 1.5725 | - |
915
+ | 0.0532 | 5320 | 1.5897 | - |
916
+ | 0.0533 | 5330 | 1.5674 | - |
917
+ | 0.0534 | 5340 | 1.5562 | - |
918
+ | 0.0535 | 5350 | 1.5582 | - |
919
+ | 0.0536 | 5360 | 1.5712 | - |
920
+ | 0.0537 | 5370 | 1.5384 | - |
921
+ | 0.0538 | 5380 | 1.5717 | - |
922
+ | 0.0539 | 5390 | 1.5793 | - |
923
+ | 0.054 | 5400 | 1.5731 | 0.5094 |
924
+ | 0.0541 | 5410 | 1.5597 | - |
925
+ | 0.0542 | 5420 | 1.5852 | - |
926
+ | 0.0543 | 5430 | 1.5932 | - |
927
+ | 0.0544 | 5440 | 1.5545 | - |
928
+ | 0.0545 | 5450 | 1.5954 | - |
929
+ | 0.0546 | 5460 | 1.5711 | - |
930
+ | 0.0547 | 5470 | 1.5441 | - |
931
+ | 0.0548 | 5480 | 1.5649 | - |
932
+ | 0.0549 | 5490 | 1.5662 | - |
933
+ | 0.055 | 5500 | 1.5433 | 0.5161 |
934
+ | 0.0551 | 5510 | 1.5714 | - |
935
+ | 0.0552 | 5520 | 1.5406 | - |
936
+ | 0.0553 | 5530 | 1.5391 | - |
937
+ | 0.0554 | 5540 | 1.5676 | - |
938
+ | 0.0555 | 5550 | 1.5568 | - |
939
+ | 0.0556 | 5560 | 1.5784 | - |
940
+ | 0.0557 | 5570 | 1.5715 | - |
941
+ | 0.0558 | 5580 | 1.5635 | - |
942
+ | 0.0559 | 5590 | 1.5659 | - |
943
+ | 0.056 | 5600 | 1.59 | 0.5109 |
944
+ | 0.0561 | 5610 | 1.5562 | - |
945
+ | 0.0562 | 5620 | 1.554 | - |
946
+ | 0.0563 | 5630 | 1.5524 | - |
947
+ | 0.0564 | 5640 | 1.5544 | - |
948
+ | 0.0565 | 5650 | 1.5805 | - |
949
+ | 0.0566 | 5660 | 1.5431 | - |
950
+ | 0.0567 | 5670 | 1.5627 | - |
951
+ | 0.0568 | 5680 | 1.5731 | - |
952
+ | 0.0569 | 5690 | 1.5795 | - |
953
+ | 0.057 | 5700 | 1.5558 | 0.5133 |
954
+ | 0.0571 | 5710 | 1.5365 | - |
955
+ | 0.0572 | 5720 | 1.5882 | - |
956
+ | 0.0573 | 5730 | 1.5812 | - |
957
+ | 0.0574 | 5740 | 1.5553 | - |
958
+ | 0.0575 | 5750 | 1.5516 | - |
959
+ | 0.0576 | 5760 | 1.5523 | - |
960
+ | 0.0577 | 5770 | 1.5522 | - |
961
+ | 0.0578 | 5780 | 1.5634 | - |
962
+ | 0.0579 | 5790 | 1.5467 | - |
963
+ | 0.058 | 5800 | 1.561 | 0.5116 |
964
+ | 0.0581 | 5810 | 1.5614 | - |
965
+ | 0.0582 | 5820 | 1.5595 | - |
966
+ | 0.0583 | 5830 | 1.5655 | - |
967
+ | 0.0584 | 5840 | 1.5842 | - |
968
+ | 0.0585 | 5850 | 1.556 | - |
969
+ | 0.0586 | 5860 | 1.5526 | - |
970
+ | 0.0587 | 5870 | 1.5637 | - |
971
+ | 0.0588 | 5880 | 1.5612 | - |
972
+ | 0.0589 | 5890 | 1.5815 | - |
973
+ | 0.059 | 5900 | 1.5407 | 0.5109 |
974
+ | 0.0591 | 5910 | 1.5594 | - |
975
+ | 0.0592 | 5920 | 1.5551 | - |
976
+ | 0.0593 | 5930 | 1.5485 | - |
977
+ | 0.0594 | 5940 | 1.5832 | - |
978
+ | 0.0595 | 5950 | 1.5729 | - |
979
+ | 0.0596 | 5960 | 1.567 | - |
980
+ | 0.0597 | 5970 | 1.5743 | - |
981
+ | 0.0598 | 5980 | 1.5456 | - |
982
+ | 0.0599 | 5990 | 1.5446 | - |
983
+ | 0.06 | 6000 | 1.5754 | 0.5088 |
984
+ | 0.0601 | 6010 | 1.5551 | - |
985
+ | 0.0602 | 6020 | 1.5345 | - |
986
+ | 0.0603 | 6030 | 1.5446 | - |
987
+ | 0.0604 | 6040 | 1.569 | - |
988
+ | 0.0605 | 6050 | 1.5573 | - |
989
+ | 0.0606 | 6060 | 1.5332 | - |
990
+ | 0.0607 | 6070 | 1.5851 | - |
991
+ | 0.0608 | 6080 | 1.5464 | - |
992
+ | 0.0609 | 6090 | 1.5552 | - |
993
+ | 0.061 | 6100 | 1.5519 | 0.5106 |
994
+ | 0.0611 | 6110 | 1.55 | - |
995
+ | 0.0612 | 6120 | 1.5674 | - |
996
+ | 0.0613 | 6130 | 1.5552 | - |
997
+ | 0.0614 | 6140 | 1.5582 | - |
998
+ | 0.0615 | 6150 | 1.5509 | - |
999
+ | 0.0616 | 6160 | 1.5531 | - |
1000
+ | 0.0617 | 6170 | 1.5411 | - |
1001
+ | 0.0618 | 6180 | 1.5569 | - |
1002
+ | 0.0619 | 6190 | 1.5531 | - |
1003
+ | 0.062 | 6200 | 1.5636 | 0.5095 |
1004
+ | 0.0621 | 6210 | 1.5427 | - |
1005
+ | 0.0622 | 6220 | 1.5796 | - |
1006
+ | 0.0623 | 6230 | 1.5355 | - |
1007
+ | 0.0624 | 6240 | 1.5428 | - |
1008
+ | 0.0625 | 6250 | 1.5297 | - |
1009
+ | 0.0626 | 6260 | 1.5397 | - |
1010
+ | 0.0627 | 6270 | 1.5606 | - |
1011
+ | 0.0628 | 6280 | 1.5366 | - |
1012
+ | 0.0629 | 6290 | 1.5391 | - |
1013
+ | 0.063 | 6300 | 1.5649 | 0.5146 |
1014
+ | 0.0631 | 6310 | 1.5612 | - |
1015
+ | 0.0632 | 6320 | 1.5357 | - |
1016
+ | 0.0633 | 6330 | 1.5406 | - |
1017
+ | 0.0634 | 6340 | 1.5636 | - |
1018
+ | 0.0635 | 6350 | 1.5423 | - |
1019
+ | 0.0636 | 6360 | 1.5265 | - |
1020
+ | 0.0637 | 6370 | 1.5507 | - |
1021
+ | 0.0638 | 6380 | 1.5481 | - |
1022
+ | 0.0639 | 6390 | 1.5464 | - |
1023
+ | 0.064 | 6400 | 1.5459 | 0.5177 |
1024
+ | 0.0641 | 6410 | 1.5542 | - |
1025
+ | 0.0642 | 6420 | 1.5515 | - |
1026
+ | 0.0643 | 6430 | 1.5741 | - |
1027
+ | 0.0644 | 6440 | 1.5338 | - |
1028
+ | 0.0645 | 6450 | 1.5527 | - |
1029
+ | 0.0646 | 6460 | 1.5624 | - |
1030
+ | 0.0647 | 6470 | 1.5469 | - |
1031
+ | 0.0648 | 6480 | 1.5486 | - |
1032
+ | 0.0649 | 6490 | 1.5582 | - |
1033
+ | 0.065 | 6500 | 1.536 | 0.5100 |
1034
+ | 0.0651 | 6510 | 1.5565 | - |
1035
+ | 0.0652 | 6520 | 1.5353 | - |
1036
+ | 0.0653 | 6530 | 1.5236 | - |
1037
+ | 0.0654 | 6540 | 1.54 | - |
1038
+ | 0.0655 | 6550 | 1.5448 | - |
1039
+ | 0.0656 | 6560 | 1.5495 | - |
1040
+ | 0.0657 | 6570 | 1.5394 | - |
1041
+ | 0.0658 | 6580 | 1.5737 | - |
1042
+ | 0.0659 | 6590 | 1.5201 | - |
1043
+ | 0.066 | 6600 | 1.5509 | 0.5127 |
1044
+ | 0.0661 | 6610 | 1.5549 | - |
1045
+ | 0.0662 | 6620 | 1.5545 | - |
1046
+ | 0.0663 | 6630 | 1.5601 | - |
1047
+ | 0.0664 | 6640 | 1.537 | - |
1048
+ | 0.0665 | 6650 | 1.5451 | - |
1049
+ | 0.0666 | 6660 | 1.5412 | - |
1050
+ | 1.0000 | 6670 | 1.5383 | - |
1051
+ | 1.0001 | 6680 | 1.5399 | - |
1052
+ | 1.0002 | 6690 | 1.5336 | - |
1053
+ | 1.0003 | 6700 | 1.5344 | 0.5156 |
1054
+ | 1.0004 | 6710 | 1.5272 | - |
1055
+ | 1.0005 | 6720 | 1.5436 | - |
1056
+ | 1.0006 | 6730 | 1.5438 | - |
1057
+ | 1.0007 | 6740 | 1.5238 | - |
1058
+ | 1.0008 | 6750 | 1.527 | - |
1059
+ | 1.0009 | 6760 | 1.5379 | - |
1060
+ | 1.0010 | 6770 | 1.5227 | - |
1061
+ | 1.0011 | 6780 | 1.5424 | - |
1062
+ | 1.0012 | 6790 | 1.5154 | - |
1063
+ | 1.0013 | 6800 | 1.5199 | 0.5088 |
1064
+ | 1.0014 | 6810 | 1.5277 | - |
1065
+ | 1.0015 | 6820 | 1.5371 | - |
1066
+ | 1.0016 | 6830 | 1.5455 | - |
1067
+ | 1.0017 | 6840 | 1.5624 | - |
1068
+ | 1.0018 | 6850 | 1.5471 | - |
1069
+ | 1.0019 | 6860 | 1.5287 | - |
1070
+ | 1.0020 | 6870 | 1.5495 | - |
1071
+ | 1.0021 | 6880 | 1.5094 | - |
1072
+ | 1.0022 | 6890 | 1.5313 | - |
1073
+ | 1.0023 | 6900 | 1.5386 | 0.5092 |
1074
+ | 1.0024 | 6910 | 1.5389 | - |
1075
+ | 1.0025 | 6920 | 1.5424 | - |
1076
+ | 1.0026 | 6930 | 1.5342 | - |
1077
+ | 1.0027 | 6940 | 1.5497 | - |
1078
+ | 1.0028 | 6950 | 1.5228 | - |
1079
+ | 1.0029 | 6960 | 1.5294 | - |
1080
+ | 1.0030 | 6970 | 1.5264 | - |
1081
+ | 1.0031 | 6980 | 1.508 | - |
1082
+ | 1.0032 | 6990 | 1.5189 | - |
1083
+ | 1.0033 | 7000 | 1.5118 | 0.5125 |
1084
+ | 1.0034 | 7010 | 1.5243 | - |
1085
+ | 1.0035 | 7020 | 1.5297 | - |
1086
+ | 1.0036 | 7030 | 1.5139 | - |
1087
+ | 1.0037 | 7040 | 1.5347 | - |
1088
+ | 1.0038 | 7050 | 1.5236 | - |
1089
+ | 1.0039 | 7060 | 1.538 | - |
1090
+ | 1.0040 | 7070 | 1.5402 | - |
1091
+ | 1.0041 | 7080 | 1.5136 | - |
1092
+ | 1.0042 | 7090 | 1.5556 | - |
1093
+ | 1.0043 | 7100 | 1.5203 | 0.5082 |
1094
+ | 1.0044 | 7110 | 1.5284 | - |
1095
+ | 1.0045 | 7120 | 1.5455 | - |
1096
+ | 1.0046 | 7130 | 1.5158 | - |
1097
+ | 1.0047 | 7140 | 1.5284 | - |
1098
+ | 1.0048 | 7150 | 1.5064 | - |
1099
+ | 1.0049 | 7160 | 1.5362 | - |
1100
+ | 1.0050 | 7170 | 1.5289 | - |
1101
+ | 1.0051 | 7180 | 1.5309 | - |
1102
+ | 1.0052 | 7190 | 1.5189 | - |
1103
+ | 1.0053 | 7200 | 1.4976 | 0.5141 |
1104
+ | 1.0054 | 7210 | 1.5245 | - |
1105
+ | 1.0055 | 7220 | 1.5435 | - |
1106
+ | 1.0056 | 7230 | 1.5165 | - |
1107
+ | 1.0057 | 7240 | 1.5469 | - |
1108
+ | 1.0058 | 7250 | 1.5298 | - |
1109
+ | 1.0059 | 7260 | 1.5141 | - |
1110
+ | 1.0060 | 7270 | 1.5313 | - |
1111
+ | 1.0061 | 7280 | 1.5166 | - |
1112
+ | 1.0062 | 7290 | 1.5311 | - |
1113
+ | 1.0063 | 7300 | 1.5188 | 0.5248 |
1114
+ | 1.0064 | 7310 | 1.5217 | - |
1115
+ | 1.0065 | 7320 | 1.4895 | - |
1116
+ | 1.0066 | 7330 | 1.5336 | - |
1117
+ | 1.0067 | 7340 | 1.5275 | - |
1118
+ | 1.0068 | 7350 | 1.5246 | - |
1119
+ | 1.0069 | 7360 | 1.5264 | - |
1120
+ | 1.0070 | 7370 | 1.5325 | - |
1121
+ | 1.0071 | 7380 | 1.5262 | - |
1122
+ | 1.0072 | 7390 | 1.5147 | - |
1123
+ | 1.0073 | 7400 | 1.515 | 0.5183 |
1124
+ | 1.0074 | 7410 | 1.5398 | - |
1125
+ | 1.0075 | 7420 | 1.5269 | - |
1126
+ | 1.0076 | 7430 | 1.5146 | - |
1127
+ | 1.0077 | 7440 | 1.5092 | - |
1128
+ | 1.0078 | 7450 | 1.5034 | - |
1129
+ | 1.0079 | 7460 | 1.5042 | - |
1130
+ | 1.0080 | 7470 | 1.5185 | - |
1131
+ | 1.0081 | 7480 | 1.537 | - |
1132
+ | 1.0082 | 7490 | 1.521 | - |
1133
+ | 1.0083 | 7500 | 1.4988 | 0.5193 |
1134
+ | 1.0084 | 7510 | 1.5177 | - |
1135
+ | 1.0085 | 7520 | 1.5335 | - |
1136
+ | 1.0086 | 7530 | 1.4924 | - |
1137
+ | 1.0087 | 7540 | 1.5419 | - |
1138
+ | 1.0088 | 7550 | 1.4974 | - |
1139
+ | 1.0089 | 7560 | 1.5227 | - |
1140
+ | 1.0090 | 7570 | 1.5242 | - |
1141
+ | 1.0091 | 7580 | 1.5008 | - |
1142
+ | 1.0092 | 7590 | 1.4978 | - |
1143
+ | 1.0093 | 7600 | 1.5043 | 0.5133 |
1144
+ | 1.0094 | 7610 | 1.5041 | - |
1145
+ | 1.0095 | 7620 | 1.5295 | - |
1146
+ | 1.0096 | 7630 | 1.5221 | - |
1147
+ | 1.0097 | 7640 | 1.502 | - |
1148
+ | 1.0098 | 7650 | 1.4861 | - |
1149
+ | 1.0099 | 7660 | 1.5108 | - |
1150
+ | 1.0100 | 7670 | 1.5257 | - |
1151
+ | 1.0101 | 7680 | 1.5215 | - |
1152
+ | 1.0102 | 7690 | 1.5413 | - |
1153
+ | 1.0103 | 7700 | 1.5071 | 0.5195 |
1154
+ | 1.0104 | 7710 | 1.5172 | - |
1155
+ | 1.0105 | 7720 | 1.531 | - |
1156
+ | 1.0106 | 7730 | 1.5104 | - |
1157
+ | 1.0107 | 7740 | 1.5009 | - |
1158
+ | 1.0108 | 7750 | 1.5046 | - |
1159
+ | 1.0109 | 7760 | 1.5339 | - |
1160
+ | 1.0110 | 7770 | 1.4935 | - |
1161
+ | 1.0111 | 7780 | 1.5211 | - |
1162
+ | 1.0112 | 7790 | 1.5203 | - |
1163
+ | 1.0113 | 7800 | 1.5066 | 0.5172 |
1164
+ | 1.0114 | 7810 | 1.5154 | - |
1165
+ | 1.0115 | 7820 | 1.5233 | - |
1166
+ | 1.0116 | 7830 | 1.5256 | - |
1167
+ | 1.0117 | 7840 | 1.5082 | - |
1168
+ | 1.0118 | 7850 | 1.5092 | - |
1169
+ | 1.0119 | 7860 | 1.5279 | - |
1170
+ | 1.0120 | 7870 | 1.5133 | - |
1171
+ | 1.0121 | 7880 | 1.5168 | - |
1172
+ | 1.0122 | 7890 | 1.4994 | - |
1173
+ | 1.0123 | 7900 | 1.5309 | 0.5175 |
1174
+ | 1.0124 | 7910 | 1.503 | - |
1175
+ | 1.0125 | 7920 | 1.4806 | - |
1176
+ | 1.0126 | 7930 | 1.5077 | - |
1177
+ | 1.0127 | 7940 | 1.4981 | - |
1178
+ | 1.0128 | 7950 | 1.5086 | - |
1179
+ | 1.0129 | 7960 | 1.5003 | - |
1180
+ | 1.0130 | 7970 | 1.5208 | - |
1181
+ | 1.0131 | 7980 | 1.4941 | - |
1182
+ | 1.0132 | 7990 | 1.5226 | - |
1183
+ | 1.0133 | 8000 | 1.5023 | 0.5128 |
1184
+ | 1.0134 | 8010 | 1.5249 | - |
1185
+ | 1.0135 | 8020 | 1.4965 | - |
1186
+ | 1.0136 | 8030 | 1.5012 | - |
1187
+ | 1.0137 | 8040 | 1.5477 | - |
1188
+ | 1.0138 | 8050 | 1.5128 | - |
1189
+ | 1.0139 | 8060 | 1.4758 | - |
1190
+ | 1.0140 | 8070 | 1.5248 | - |
1191
+ | 1.0141 | 8080 | 1.5497 | - |
1192
+ | 1.0142 | 8090 | 1.5281 | - |
1193
+ | 1.0143 | 8100 | 1.5217 | 0.5182 |
1194
+ | 1.0144 | 8110 | 1.4934 | - |
1195
+ | 1.0145 | 8120 | 1.527 | - |
1196
+ | 1.0146 | 8130 | 1.522 | - |
1197
+ | 1.0147 | 8140 | 1.4958 | - |
1198
+ | 1.0148 | 8150 | 1.5086 | - |
1199
+ | 1.0149 | 8160 | 1.5038 | - |
1200
+ | 1.0150 | 8170 | 1.4971 | - |
1201
+ | 1.0151 | 8180 | 1.5217 | - |
1202
+ | 1.0152 | 8190 | 1.4871 | - |
1203
+ | 1.0153 | 8200 | 1.525 | 0.5223 |
1204
+ | 1.0154 | 8210 | 1.5179 | - |
1205
+ | 1.0155 | 8220 | 1.4958 | - |
1206
+ | 1.0156 | 8230 | 1.5119 | - |
1207
+ | 1.0157 | 8240 | 1.5116 | - |
1208
+ | 1.0158 | 8250 | 1.4997 | - |
1209
+ | 1.0159 | 8260 | 1.5089 | - |
1210
+ | 1.0160 | 8270 | 1.5377 | - |
1211
+ | 1.0161 | 8280 | 1.502 | - |
1212
+ | 1.0162 | 8290 | 1.486 | - |
1213
+ | 1.0163 | 8300 | 1.5065 | 0.5142 |
1214
+ | 1.0164 | 8310 | 1.5047 | - |
1215
+ | 1.0165 | 8320 | 1.5028 | - |
1216
+ | 1.0166 | 8330 | 1.4898 | - |
1217
+ | 1.0167 | 8340 | 1.5046 | - |
1218
+ | 1.0168 | 8350 | 1.5195 | - |
1219
+ | 1.0169 | 8360 | 1.4848 | - |
1220
+ | 1.0170 | 8370 | 1.5242 | - |
1221
+ | 1.0171 | 8380 | 1.4994 | - |
1222
+ | 1.0172 | 8390 | 1.5306 | - |
1223
+ | 1.0173 | 8400 | 1.5079 | 0.5113 |
1224
+ | 1.0174 | 8410 | 1.5143 | - |
1225
+ | 1.0175 | 8420 | 1.495 | - |
1226
+ | 1.0176 | 8430 | 1.492 | - |
1227
+ | 1.0177 | 8440 | 1.5087 | - |
1228
+ | 1.0178 | 8450 | 1.4765 | - |
1229
+ | 1.0179 | 8460 | 1.5043 | - |
1230
+ | 1.0180 | 8470 | 1.5014 | - |
1231
+ | 1.0181 | 8480 | 1.5275 | - |
1232
+ | 1.0182 | 8490 | 1.5101 | - |
1233
+ | 1.0183 | 8500 | 1.5117 | 0.5129 |
1234
+ | 1.0184 | 8510 | 1.5254 | - |
1235
+ | 1.0185 | 8520 | 1.5261 | - |
1236
+ | 1.0186 | 8530 | 1.5111 | - |
1237
+ | 1.0187 | 8540 | 1.5176 | - |
1238
+ | 1.0188 | 8550 | 1.4906 | - |
1239
+ | 1.0189 | 8560 | 1.5139 | - |
1240
+ | 1.0190 | 8570 | 1.4893 | - |
1241
+ | 1.0191 | 8580 | 1.5022 | - |
1242
+ | 1.0192 | 8590 | 1.5134 | - |
1243
+ | 1.0193 | 8600 | 1.5087 | 0.5143 |
1244
+ | 1.0194 | 8610 | 1.512 | - |
1245
+ | 1.0195 | 8620 | 1.495 | - |
1246
+ | 1.0196 | 8630 | 1.4875 | - |
1247
+ | 1.0197 | 8640 | 1.5239 | - |
1248
+ | 1.0198 | 8650 | 1.513 | - |
1249
+ | 1.0199 | 8660 | 1.5124 | - |
1250
+ | 1.0200 | 8670 | 1.4855 | - |
1251
+ | 1.0201 | 8680 | 1.5028 | - |
1252
+ | 1.0202 | 8690 | 1.4998 | - |
1253
+ | 1.0203 | 8700 | 1.4937 | 0.5149 |
1254
+ | 1.0204 | 8710 | 1.5103 | - |
1255
+ | 1.0205 | 8720 | 1.5105 | - |
1256
+ | 1.0206 | 8730 | 1.5062 | - |
1257
+ | 1.0207 | 8740 | 1.5228 | - |
1258
+ | 1.0208 | 8750 | 1.4866 | - |
1259
+ | 1.0209 | 8760 | 1.5092 | - |
1260
+ | 1.0210 | 8770 | 1.5088 | - |
1261
+ | 1.0211 | 8780 | 1.488 | - |
1262
+ | 1.0212 | 8790 | 1.5022 | - |
1263
+ | 1.0213 | 8800 | 1.504 | 0.5097 |
1264
+ | 1.0214 | 8810 | 1.5051 | - |
1265
+ | 1.0215 | 8820 | 1.5027 | - |
1266
+ | 1.0216 | 8830 | 1.5273 | - |
1267
+ | 1.0217 | 8840 | 1.4884 | - |
1268
+ | 1.0218 | 8850 | 1.4908 | - |
1269
+ | 1.0219 | 8860 | 1.5074 | - |
1270
+ | 1.0220 | 8870 | 1.516 | - |
1271
+ | 1.0221 | 8880 | 1.5422 | - |
1272
+ | 1.0222 | 8890 | 1.5174 | - |
1273
+ | 1.0223 | 8900 | 1.5009 | 0.5129 |
1274
+ | 1.0224 | 8910 | 1.4936 | - |
1275
+ | 1.0225 | 8920 | 1.5076 | - |
1276
+ | 1.0226 | 8930 | 1.5093 | - |
1277
+ | 1.0227 | 8940 | 1.4848 | - |
1278
+ | 1.0228 | 8950 | 1.4958 | - |
1279
+ | 1.0229 | 8960 | 1.5092 | - |
1280
+ | 1.0230 | 8970 | 1.5048 | - |
1281
+ | 1.0231 | 8980 | 1.4895 | - |
1282
+ | 1.0232 | 8990 | 1.4949 | - |
1283
+ | 1.0233 | 9000 | 1.4988 | 0.5131 |
1284
+ | 1.0234 | 9010 | 1.5205 | - |
1285
+ | 1.0235 | 9020 | 1.4961 | - |
1286
+ | 1.0236 | 9030 | 1.5235 | - |
1287
+ | 1.0237 | 9040 | 1.4945 | - |
1288
+ | 1.0238 | 9050 | 1.504 | - |
1289
+ | 1.0239 | 9060 | 1.5147 | - |
1290
+ | 1.0240 | 9070 | 1.5104 | - |
1291
+ | 1.0241 | 9080 | 1.4981 | - |
1292
+ | 1.0242 | 9090 | 1.5019 | - |
1293
+ | 1.0243 | 9100 | 1.4843 | 0.5163 |
1294
+ | 1.0244 | 9110 | 1.4691 | - |
1295
+ | 1.0245 | 9120 | 1.5097 | - |
1296
+ | 1.0246 | 9130 | 1.524 | - |
1297
+ | 1.0247 | 9140 | 1.5055 | - |
1298
+ | 1.0248 | 9150 | 1.5212 | - |
1299
+ | 1.0249 | 9160 | 1.4888 | - |
1300
+ | 1.0250 | 9170 | 1.4887 | - |
1301
+ | 1.0251 | 9180 | 1.5051 | - |
1302
+ | 1.0252 | 9190 | 1.491 | - |
1303
+ | 1.0253 | 9200 | 1.492 | 0.5140 |
1304
+ | 1.0254 | 9210 | 1.4771 | - |
1305
+ | 1.0255 | 9220 | 1.4683 | - |
1306
+ | 1.0256 | 9230 | 1.4824 | - |
1307
+ | 1.0257 | 9240 | 1.4967 | - |
1308
+ | 1.0258 | 9250 | 1.5054 | - |
1309
+ | 1.0259 | 9260 | 1.4884 | - |
1310
+ | 1.0260 | 9270 | 1.4975 | - |
1311
+ | 1.0261 | 9280 | 1.4918 | - |
1312
+ | 1.0262 | 9290 | 1.5143 | - |
1313
+ | 1.0263 | 9300 | 1.4946 | 0.5168 |
1314
+ | 1.0264 | 9310 | 1.4905 | - |
1315
+ | 1.0265 | 9320 | 1.4981 | - |
1316
+ | 1.0266 | 9330 | 1.4591 | - |
1317
+ | 1.0267 | 9340 | 1.4933 | - |
1318
+ | 1.0268 | 9350 | 1.5028 | - |
1319
+ | 1.0269 | 9360 | 1.5035 | - |
1320
+ | 1.0270 | 9370 | 1.4996 | - |
1321
+ | 1.0271 | 9380 | 1.4939 | - |
1322
+ | 1.0272 | 9390 | 1.505 | - |
1323
+ | 1.0273 | 9400 | 1.496 | 0.5120 |
1324
+ | 1.0274 | 9410 | 1.5085 | - |
1325
+ | 1.0275 | 9420 | 1.5057 | - |
1326
+ | 1.0276 | 9430 | 1.5065 | - |
1327
+ | 1.0277 | 9440 | 1.4898 | - |
1328
+ | 1.0278 | 9450 | 1.5031 | - |
1329
+ | 1.0279 | 9460 | 1.4784 | - |
1330
+ | 1.0280 | 9470 | 1.4906 | - |
1331
+ | 1.0281 | 9480 | 1.4878 | - |
1332
+ | 1.0282 | 9490 | 1.4931 | - |
1333
+ | 1.0283 | 9500 | 1.5035 | 0.5159 |
1334
+ | 1.0284 | 9510 | 1.4766 | - |
1335
+ | 1.0285 | 9520 | 1.497 | - |
1336
+ | 1.0286 | 9530 | 1.4965 | - |
1337
+ | 1.0287 | 9540 | 1.4743 | - |
1338
+ | 1.0288 | 9550 | 1.4836 | - |
1339
+ | 1.0289 | 9560 | 1.5031 | - |
1340
+ | 1.0290 | 9570 | 1.5287 | - |
1341
+ | 1.0291 | 9580 | 1.4851 | - |
1342
+ | 1.0292 | 9590 | 1.5061 | - |
1343
+ | 1.0293 | 9600 | 1.5071 | 0.5191 |
1344
+ | 1.0294 | 9610 | 1.4943 | - |
1345
+ | 1.0295 | 9620 | 1.502 | - |
1346
+ | 1.0296 | 9630 | 1.4953 | - |
1347
+ | 1.0297 | 9640 | 1.4923 | - |
1348
+ | 1.0298 | 9650 | 1.489 | - |
1349
+ | 1.0299 | 9660 | 1.4968 | - |
1350
+ | 1.0300 | 9670 | 1.5051 | - |
1351
+ | 1.0301 | 9680 | 1.4736 | - |
1352
+ | 1.0302 | 9690 | 1.4883 | - |
1353
+ | 1.0303 | 9700 | 1.5062 | 0.5145 |
1354
+ | 1.0304 | 9710 | 1.4961 | - |
1355
+ | 1.0305 | 9720 | 1.5107 | - |
1356
+ | 1.0306 | 9730 | 1.4852 | - |
1357
+ | 1.0307 | 9740 | 1.4771 | - |
1358
+ | 1.0308 | 9750 | 1.4911 | - |
1359
+ | 1.0309 | 9760 | 1.4781 | - |
1360
+ | 1.0310 | 9770 | 1.5064 | - |
1361
+ | 1.0311 | 9780 | 1.4871 | - |
1362
+ | 1.0312 | 9790 | 1.5042 | - |
1363
+ | 1.0313 | 9800 | 1.4922 | 0.5190 |
1364
+ | 1.0314 | 9810 | 1.4952 | - |
1365
+ | 1.0315 | 9820 | 1.4875 | - |
1366
+ | 1.0316 | 9830 | 1.4974 | - |
1367
+ | 1.0317 | 9840 | 1.5013 | - |
1368
+ | 1.0318 | 9850 | 1.4966 | - |
1369
+ | 1.0319 | 9860 | 1.4861 | - |
1370
+ | 1.0320 | 9870 | 1.5135 | - |
1371
+ | 1.0321 | 9880 | 1.5038 | - |
1372
+ | 1.0322 | 9890 | 1.5148 | - |
1373
+ | 1.0323 | 9900 | 1.5164 | 0.5195 |
1374
+ | 1.0324 | 9910 | 1.494 | - |
1375
+ | 1.0325 | 9920 | 1.4848 | - |
1376
+ | 1.0326 | 9930 | 1.5015 | - |
1377
+ | 1.0327 | 9940 | 1.4868 | - |
1378
+ | 1.0328 | 9950 | 1.4728 | - |
1379
+ | 1.0329 | 9960 | 1.4989 | - |
1380
+ | 1.0330 | 9970 | 1.4742 | - |
1381
+ | 1.0331 | 9980 | 1.4743 | - |
1382
+ | 1.0332 | 9990 | 1.4996 | - |
1383
+ | 1.0333 | 10000 | 1.4748 | 0.5134 |
1384
+
1385
+ </details>
1386
+
1387
+ ### Framework Versions
1388
+ - Python: 3.12.12
1389
+ - Sentence Transformers: 5.2.0
1390
+ - Transformers: 4.57.3
1391
+ - PyTorch: 2.9.0+cu128
1392
+ - Accelerate: 1.12.0
1393
+ - Datasets: 4.4.2
1394
+ - Tokenizers: 0.22.1
1395
+
1396
+ ## Citation
1397
+
1398
+ ### BibTeX
1399
+
1400
+ #### Sentence Transformers
1401
+ ```bibtex
1402
+ @inproceedings{reimers-2019-sentence-bert,
1403
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1404
+ author = "Reimers, Nils and Gurevych, Iryna",
1405
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1406
+ month = "11",
1407
+ year = "2019",
1408
+ publisher = "Association for Computational Linguistics",
1409
+ url = "https://arxiv.org/abs/1908.10084",
1410
+ }
1411
+ ```
1412
+
1413
+ #### CachedMultipleNegativesRankingLoss
1414
+ ```bibtex
1415
+ @misc{gao2021scaling,
1416
+ title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
1417
+ author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
1418
+ year={2021},
1419
+ eprint={2101.06983},
1420
+ archivePrefix={arXiv},
1421
+ primaryClass={cs.LG}
1422
+ }
1423
+ ```
1424
+
1425
+ <!--
1426
+ ## Glossary
1427
+
1428
+ *Clearly define terms in order to be accessible across audiences.*
1429
+ -->
1430
+
1431
+ <!--
1432
+ ## Model Card Authors
1433
+
1434
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1435
+ -->
1436
+
1437
+ <!--
1438
+ ## Model Card Contact
1439
+
1440
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1441
+ -->
added_tokens.json ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "</think>": 151668,
3
+ "</tool_call>": 151658,
4
+ "</tool_response>": 151666,
5
+ "<think>": 151667,
6
+ "<tool_call>": 151657,
7
+ "<tool_response>": 151665,
8
+ "<|box_end|>": 151649,
9
+ "<|box_start|>": 151648,
10
+ "<|endoftext|>": 151643,
11
+ "<|file_sep|>": 151664,
12
+ "<|fim_middle|>": 151660,
13
+ "<|fim_pad|>": 151662,
14
+ "<|fim_prefix|>": 151659,
15
+ "<|fim_suffix|>": 151661,
16
+ "<|im_end|>": 151645,
17
+ "<|im_start|>": 151644,
18
+ "<|image_pad|>": 151655,
19
+ "<|object_ref_end|>": 151647,
20
+ "<|object_ref_start|>": 151646,
21
+ "<|quad_end|>": 151651,
22
+ "<|quad_start|>": 151650,
23
+ "<|repo_name|>": 151663,
24
+ "<|video_pad|>": 151656,
25
+ "<|vision_end|>": 151653,
26
+ "<|vision_pad|>": 151654,
27
+ "<|vision_start|>": 151652
28
+ }
chat_template.jinja ADDED
@@ -0,0 +1,85 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {%- if tools %}
2
+ {{- '<|im_start|>system\n' }}
3
+ {%- if messages[0].role == 'system' %}
4
+ {{- messages[0].content + '\n\n' }}
5
+ {%- endif %}
6
+ {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
7
+ {%- for tool in tools %}
8
+ {{- "\n" }}
9
+ {{- tool | tojson }}
10
+ {%- endfor %}
11
+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
12
+ {%- else %}
13
+ {%- if messages[0].role == 'system' %}
14
+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
15
+ {%- endif %}
16
+ {%- endif %}
17
+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
18
+ {%- for message in messages[::-1] %}
19
+ {%- set index = (messages|length - 1) - loop.index0 %}
20
+ {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
21
+ {%- set ns.multi_step_tool = false %}
22
+ {%- set ns.last_query_index = index %}
23
+ {%- endif %}
24
+ {%- endfor %}
25
+ {%- for message in messages %}
26
+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
27
+ {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
28
+ {%- elif message.role == "assistant" %}
29
+ {%- set content = message.content %}
30
+ {%- set reasoning_content = '' %}
31
+ {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
32
+ {%- set reasoning_content = message.reasoning_content %}
33
+ {%- else %}
34
+ {%- if '</think>' in message.content %}
35
+ {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
36
+ {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
37
+ {%- endif %}
38
+ {%- endif %}
39
+ {%- if loop.index0 > ns.last_query_index %}
40
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+ "151658": {
126
+ "content": "</tool_call>",
127
+ "lstrip": false,
128
+ "normalized": false,
129
+ "rstrip": false,
130
+ "single_word": false,
131
+ "special": false
132
+ },
133
+ "151659": {
134
+ "content": "<|fim_prefix|>",
135
+ "lstrip": false,
136
+ "normalized": false,
137
+ "rstrip": false,
138
+ "single_word": false,
139
+ "special": false
140
+ },
141
+ "151660": {
142
+ "content": "<|fim_middle|>",
143
+ "lstrip": false,
144
+ "normalized": false,
145
+ "rstrip": false,
146
+ "single_word": false,
147
+ "special": false
148
+ },
149
+ "151661": {
150
+ "content": "<|fim_suffix|>",
151
+ "lstrip": false,
152
+ "normalized": false,
153
+ "rstrip": false,
154
+ "single_word": false,
155
+ "special": false
156
+ },
157
+ "151662": {
158
+ "content": "<|fim_pad|>",
159
+ "lstrip": false,
160
+ "normalized": false,
161
+ "rstrip": false,
162
+ "single_word": false,
163
+ "special": false
164
+ },
165
+ "151663": {
166
+ "content": "<|repo_name|>",
167
+ "lstrip": false,
168
+ "normalized": false,
169
+ "rstrip": false,
170
+ "single_word": false,
171
+ "special": false
172
+ },
173
+ "151664": {
174
+ "content": "<|file_sep|>",
175
+ "lstrip": false,
176
+ "normalized": false,
177
+ "rstrip": false,
178
+ "single_word": false,
179
+ "special": false
180
+ },
181
+ "151665": {
182
+ "content": "<tool_response>",
183
+ "lstrip": false,
184
+ "normalized": false,
185
+ "rstrip": false,
186
+ "single_word": false,
187
+ "special": false
188
+ },
189
+ "151666": {
190
+ "content": "</tool_response>",
191
+ "lstrip": false,
192
+ "normalized": false,
193
+ "rstrip": false,
194
+ "single_word": false,
195
+ "special": false
196
+ },
197
+ "151667": {
198
+ "content": "<think>",
199
+ "lstrip": false,
200
+ "normalized": false,
201
+ "rstrip": false,
202
+ "single_word": false,
203
+ "special": false
204
+ },
205
+ "151668": {
206
+ "content": "</think>",
207
+ "lstrip": false,
208
+ "normalized": false,
209
+ "rstrip": false,
210
+ "single_word": false,
211
+ "special": false
212
+ }
213
+ },
214
+ "additional_special_tokens": [
215
+ "<|im_start|>",
216
+ "<|im_end|>",
217
+ "<|object_ref_start|>",
218
+ "<|object_ref_end|>",
219
+ "<|box_start|>",
220
+ "<|box_end|>",
221
+ "<|quad_start|>",
222
+ "<|quad_end|>",
223
+ "<|vision_start|>",
224
+ "<|vision_end|>",
225
+ "<|vision_pad|>",
226
+ "<|image_pad|>",
227
+ "<|video_pad|>"
228
+ ],
229
+ "bos_token": null,
230
+ "clean_up_tokenization_spaces": false,
231
+ "eos_token": "<|endoftext|>",
232
+ "errors": "replace",
233
+ "extra_special_tokens": {},
234
+ "max_length": 32768,
235
+ "model_max_length": 32768,
236
+ "pad_to_multiple_of": null,
237
+ "pad_token": "<|endoftext|>",
238
+ "pad_token_type_id": 0,
239
+ "padding_side": "right",
240
+ "split_special_tokens": false,
241
+ "stride": 0,
242
+ "tokenizer_class": "Qwen2Tokenizer",
243
+ "truncation_side": "right",
244
+ "truncation_strategy": "longest_first",
245
+ "unk_token": null
246
+ }
vocab.json ADDED
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