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hanwenzhu
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all-distilroberta-v1-lr2e-4-bs256-nneg3-ml-ne5-mar17

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:5817740
loss:MaskedCachedMultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
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hanwenzhu commited on
Commit
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Add new SentenceTransformer model.

Browse files
Files changed (12) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +1298 -0
  3. config.json +28 -0
  4. config_sentence_transformers.json +10 -0
  5. merges.txt +0 -0
  6. model.safetensors +3 -0
  7. modules.json +20 -0
  8. sentence_bert_config.json +4 -0
  9. special_tokens_map.json +51 -0
  10. tokenizer.json +0 -0
  11. tokenizer_config.json +64 -0
  12. vocab.json +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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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": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,1298 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: sentence-transformers/all-distilroberta-v1
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+ library_name: sentence-transformers
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+ pipeline_tag: sentence-similarity
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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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+ - generated_from_trainer
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+ - dataset_size:5817740
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+ - loss:MaskedCachedMultipleNegativesRankingLoss
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+ widget:
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+ - source_sentence: Mathlib.Algebra.Group.Pointwise.Finset.Basic#679
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+ sentences:
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+ - instContinuousStarReal
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+ - StrictOrderedSemiring.toMulPosStrictMono
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+ - IsCancelAdd.toIsLeftCancelAdd
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+ - source_sentence: Mathlib.Algebra.MvPolynomial.Degrees#315
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+ sentences:
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+ - Algebra.smul_def
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+ - IsLocalMinOn.hasFDerivWithinAt_nonneg
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+ - CategoryTheory.GlueData.t_fac
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+ - source_sentence: Mathlib.Algebra.Group.Pointwise.Finset.Basic#679
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+ sentences:
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+ - eq_of_heq
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+ - add_right_injective
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+ - Summable.of_norm_bounded_eventually_nat
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+ - source_sentence: Mathlib.Algebra.Polynomial.FieldDivision#94
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+ sentences:
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+ - Polynomial.coe_normUnit
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+ - Nat.instCharZero
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+ - Multiset.map_congr
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+ - source_sentence: Mathlib.Analysis.SpecialFunctions.Complex.LogDeriv#35
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+ sentences:
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+ - Nat.cast_zero
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+ - Function.Injective.eq_iff
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+ - HasDerivAt.clog
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/all-distilroberta-v1
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1). It maps sentences & paragraphs to a 768-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:** [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) <!-- at revision 842eaed40bee4d61673a81c92d5689a8fed7a09f -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 768 tokens
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+ - **Similarity Function:** Cosine Similarity
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+ <!-- - **Training Dataset:** Unknown -->
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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/UKPLab/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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
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+ (1): Pooling({'word_embedding_dimension': 768, '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': False, 'include_prompt': True})
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+ (2): Normalize()
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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("hanwenzhu/all-distilroberta-v1-lr2e-4-bs256-nneg3-ml-ne5-mar17")
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+ # Run inference
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+ sentences = [
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+ 'Mathlib.Analysis.SpecialFunctions.Complex.LogDeriv#35',
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+ 'HasDerivAt.clog',
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+ 'Nat.cast_zero',
93
+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 768]
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+
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+ # Get the similarity scores for the embeddings
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+ similarities = model.similarity(embeddings, embeddings)
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+ print(similarities.shape)
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+ # [3, 3]
102
+ ```
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+
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+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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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>
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 5,817,740 training samples
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+ * Columns: <code>state_name</code> and <code>premise_name</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | state_name | premise_name |
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+ |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
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+ | type | string | string |
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+ | details | <ul><li>min: 11 tokens</li><li>mean: 16.44 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.9 tokens</li><li>max: 50 tokens</li></ul> |
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+ * Samples:
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+ | state_name | premise_name |
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+ |:----------------------------------------------|:-----------------------------------|
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+ | <code>Mathlib.Algebra.Field.IsField#12</code> | <code>Classical.choose_spec</code> |
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+ | <code>Mathlib.Algebra.Field.IsField#12</code> | <code>IsField.mul_comm</code> |
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+ | <code>Mathlib.Algebra.Field.IsField#12</code> | <code>eq_of_heq</code> |
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+ * Loss: <code>loss.MaskedCachedMultipleNegativesRankingLoss</code> with these parameters:
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+ ```json
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+ {
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+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
165
+ }
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+ ```
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+
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+ ### Evaluation Dataset
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+
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+ #### Unnamed Dataset
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+
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+
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+ * Size: 1,959 evaluation samples
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+ * Columns: <code>state_name</code> and <code>premise_name</code>
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+ * Approximate statistics based on the first 1000 samples:
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+ | | state_name | premise_name |
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+ |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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+ | type | string | string |
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+ | details | <ul><li>min: 10 tokens</li><li>mean: 17.08 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.05 tokens</li><li>max: 31 tokens</li></ul> |
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+ * Samples:
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+ | state_name | premise_name |
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+ |:-------------------------------------------------------------|:----------------------------------------------------------|
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+ | <code>Mathlib.Algebra.Algebra.Hom#80</code> | <code>AlgHom.commutes</code> |
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+ | <code>Mathlib.Algebra.Algebra.NonUnitalSubalgebra#237</code> | <code>NonUnitalAlgHom.instNonUnitalAlgSemiHomClass</code> |
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+ | <code>Mathlib.Algebra.Algebra.NonUnitalSubalgebra#237</code> | <code>NonUnitalAlgebra.mem_top</code> |
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+ * Loss: <code>loss.MaskedCachedMultipleNegativesRankingLoss</code> with these parameters:
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+ ```json
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+ {
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+ "scale": 20.0,
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+ "similarity_fct": "cos_sim"
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+ }
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+ ```
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+
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+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 256
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+ - `per_device_eval_batch_size`: 64
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+ - `learning_rate`: 0.0002
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+ - `num_train_epochs`: 5.0
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+ - `lr_scheduler_type`: cosine
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+ - `warmup_ratio`: 0.03
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+ - `bf16`: True
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+ - `dataloader_num_workers`: 4
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+ - `resume_from_checkpoint`: /data/user_data/thomaszh/models/all-distilroberta-v1-lr2e-4-bs256-nneg3-ml-ne5/checkpoint-104604
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 256
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+ - `per_device_eval_batch_size`: 64
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 0.0002
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 5.0
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: cosine
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.03
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
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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
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: True
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+ - `fp16`: False
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 4
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `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}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: /data/user_data/thomaszh/models/all-distilroberta-v1-lr2e-4-bs256-nneg3-ml-ne5/checkpoint-104604
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: False
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+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
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+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
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+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
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+ - `split_batches`: None
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+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
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+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: False
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+ - `use_liger_kernel`: False
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+ - `eval_use_gather_object`: False
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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
323
+
324
+ </details>
325
+
326
+ ### Training Logs
327
+ <details><summary>Click to expand</summary>
328
+
329
+ | Epoch | Step | Training Loss | loss |
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+ |:------:|:------:|:-------------:|:------:|
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+ | 4.6031 | 104610 | 0.4939 | - |
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+ | 4.6035 | 104620 | 0.4904 | - |
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+ | 4.6040 | 104630 | 0.481 | - |
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+ | 4.6044 | 104640 | 0.486 | - |
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+ | 4.6049 | 104650 | 0.4596 | - |
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+ | 4.6053 | 104660 | 0.4864 | - |
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+ | 4.6057 | 104670 | 0.4577 | - |
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+ | 4.6062 | 104680 | 0.4646 | - |
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+ | 4.6066 | 104690 | 0.4478 | - |
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+ | 4.6071 | 104700 | 0.4844 | - |
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+ | 4.6075 | 104710 | 0.4836 | - |
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+ | 4.6079 | 104720 | 0.4445 | - |
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+ | 4.6084 | 104730 | 0.4883 | - |
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+ | 4.6088 | 104740 | 0.5054 | - |
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+ | 4.6093 | 104750 | 0.4992 | - |
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+ | 4.6097 | 104760 | 0.4759 | - |
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+ | 4.6101 | 104770 | 0.483 | - |
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+ | 4.6106 | 104780 | 0.4668 | - |
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+ | 4.6110 | 104790 | 0.4839 | - |
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+ | 4.6115 | 104800 | 0.4426 | - |
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+ | 4.6119 | 104810 | 0.4851 | - |
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+ | 4.6123 | 104820 | 0.4837 | - |
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+ | 4.6128 | 104830 | 0.4728 | - |
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+ | 4.6132 | 104840 | 0.4796 | - |
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+ | 4.6137 | 104850 | 0.4824 | - |
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+ | 4.6141 | 104860 | 0.4948 | - |
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+ | 4.6145 | 104870 | 0.4902 | - |
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+ | 4.6150 | 104880 | 0.4565 | - |
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+ | 4.6154 | 104890 | 0.5068 | - |
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+ | 4.6159 | 104900 | 0.4881 | - |
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+ | 4.6163 | 104910 | 0.5064 | - |
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+ | 4.6167 | 104920 | 0.4877 | - |
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+ | 4.6172 | 104930 | 0.498 | - |
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+ | 4.6176 | 104940 | 0.478 | - |
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+ | 4.6181 | 104950 | 0.4972 | - |
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+ | 4.6185 | 104960 | 0.4654 | - |
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+ | 4.6189 | 104970 | 0.4544 | - |
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+ | 4.6194 | 104980 | 0.477 | - |
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+ | 4.6198 | 104990 | 0.4957 | - |
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+ | 4.6203 | 105000 | 0.4695 | - |
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+ | 4.6207 | 105010 | 0.4927 | - |
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+ | 4.6211 | 105020 | 0.4805 | - |
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+ | 4.6216 | 105030 | 0.4929 | - |
374
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375
+ | 4.6225 | 105050 | 0.4814 | - |
376
+ | 4.6229 | 105060 | 0.464 | - |
377
+ | 4.6233 | 105070 | 0.4752 | - |
378
+ | 4.6238 | 105080 | 0.4609 | - |
379
+ | 4.6242 | 105090 | 0.4754 | - |
380
+ | 4.6247 | 105100 | 0.48 | - |
381
+ | 4.6251 | 105110 | 0.4587 | - |
382
+ | 4.6255 | 105120 | 0.4709 | - |
383
+ | 4.6260 | 105130 | 0.4775 | - |
384
+ | 4.6264 | 105140 | 0.4856 | - |
385
+ | 4.6269 | 105150 | 0.5094 | - |
386
+ | 4.6273 | 105160 | 0.4857 | - |
387
+ | 4.6277 | 105170 | 0.4826 | - |
388
+ | 4.6282 | 105180 | 0.4755 | - |
389
+ | 4.6286 | 105190 | 0.478 | - |
390
+ | 4.6291 | 105200 | 0.4653 | - |
391
+ | 4.6295 | 105210 | 0.4846 | - |
392
+ | 4.6299 | 105220 | 0.495 | - |
393
+ | 4.6304 | 105230 | 0.4818 | - |
394
+ | 4.6308 | 105240 | 0.4774 | - |
395
+ | 4.6313 | 105250 | 0.4653 | - |
396
+ | 4.6317 | 105260 | 0.4831 | - |
397
+ | 4.6321 | 105270 | 0.4669 | - |
398
+ | 4.6326 | 105280 | 0.487 | - |
399
+ | 4.6330 | 105290 | 0.4782 | - |
400
+ | 4.6335 | 105300 | 0.4856 | - |
401
+ | 4.6339 | 105310 | 0.4788 | - |
402
+ | 4.6343 | 105320 | 0.4645 | - |
403
+ | 4.6348 | 105330 | 0.4584 | - |
404
+ | 4.6352 | 105340 | 0.4794 | - |
405
+ | 4.6357 | 105350 | 0.4689 | - |
406
+ | 4.6361 | 105360 | 0.4987 | - |
407
+ | 4.6365 | 105370 | 0.4593 | - |
408
+ | 4.6370 | 105380 | 0.4912 | - |
409
+ | 4.6374 | 105390 | 0.468 | - |
410
+ | 4.6379 | 105400 | 0.487 | - |
411
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412
+ | 4.6387 | 105420 | 0.4561 | - |
413
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414
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415
+ | 4.6401 | 105450 | 0.4885 | - |
416
+ | 4.6405 | 105460 | 0.4705 | - |
417
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418
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419
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420
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421
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422
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423
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424
+ | 4.6440 | 105540 | 0.4914 | - |
425
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426
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427
+ | 4.6453 | 105570 | 0.478 | - |
428
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429
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430
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431
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432
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433
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434
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435
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436
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437
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438
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439
+ | 4.6506 | 105690 | 0.489 | - |
440
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441
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442
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443
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444
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445
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446
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447
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448
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449
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
+ | 4.6638 | 105990 | 0.4958 | - |
471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
+ | 4.6700 | 106130 | 0.4983 | - |
485
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486
+ | 4.6709 | 106150 | 0.4656 | - |
487
+ | 4.6713 | 106160 | 0.4967 | - |
488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
+ | 4.6761 | 106270 | 0.4966 | - |
499
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500
+ | 4.6770 | 106290 | 0.4678 | - |
501
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502
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503
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504
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505
+ | 4.6792 | 106340 | 0.4784 | - |
506
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507
+ | 4.6801 | 106360 | 0.483 | - |
508
+ | 4.6805 | 106370 | 0.4846 | - |
509
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510
+ | 4.6814 | 106390 | 0.4573 | - |
511
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512
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513
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514
+ | 4.6832 | 106430 | 0.499 | - |
515
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516
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517
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518
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519
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520
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521
+ | 4.6863 | 106500 | 0.492 | - |
522
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523
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524
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525
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526
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527
+ | 4.6889 | 106560 | 0.4539 | - |
528
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529
+ | 4.6898 | 106580 | 0.4763 | - |
530
+ | 4.6902 | 106590 | 0.4708 | - |
531
+ | 4.6907 | 106600 | 0.4594 | - |
532
+ | 4.6911 | 106610 | 0.477 | - |
533
+ | 4.6915 | 106620 | 0.471 | - |
534
+ | 4.6920 | 106630 | 0.4766 | - |
535
+ | 4.6924 | 106640 | 0.5066 | - |
536
+ | 4.6929 | 106650 | 0.5013 | - |
537
+ | 4.6933 | 106660 | 0.4733 | - |
538
+ | 4.6937 | 106670 | 0.4751 | - |
539
+ | 4.6942 | 106680 | 0.4794 | - |
540
+ | 4.6946 | 106690 | 0.4897 | - |
541
+ | 4.6951 | 106700 | 0.483 | - |
542
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543
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544
+ | 4.6964 | 106730 | 0.4627 | - |
545
+ | 4.6968 | 106740 | 0.4728 | - |
546
+ | 4.6973 | 106750 | 0.4698 | - |
547
+ | 4.6977 | 106760 | 0.4787 | - |
548
+ | 4.6981 | 106770 | 0.474 | - |
549
+ | 4.6986 | 106780 | 0.4667 | - |
550
+ | 4.6990 | 106790 | 0.4879 | - |
551
+ | 4.6995 | 106800 | 0.4994 | - |
552
+ | 4.6999 | 106810 | 0.4989 | - |
553
+ | 4.7003 | 106820 | 0.4592 | - |
554
+ | 4.7008 | 106830 | 0.4613 | - |
555
+ | 4.7012 | 106840 | 0.4904 | - |
556
+ | 4.7017 | 106850 | 0.4727 | - |
557
+ | 4.7021 | 106860 | 0.4681 | - |
558
+ | 4.7025 | 106870 | 0.4785 | - |
559
+ | 4.7029 | 106878 | - | 1.2603 |
560
+ | 4.7030 | 106880 | 0.4598 | - |
561
+ | 4.7034 | 106890 | 0.49 | - |
562
+ | 4.7039 | 106900 | 0.4809 | - |
563
+ | 4.7043 | 106910 | 0.5019 | - |
564
+ | 4.7047 | 106920 | 0.4417 | - |
565
+ | 4.7052 | 106930 | 0.4856 | - |
566
+ | 4.7056 | 106940 | 0.4656 | - |
567
+ | 4.7061 | 106950 | 0.5102 | - |
568
+ | 4.7065 | 106960 | 0.4836 | - |
569
+ | 4.7069 | 106970 | 0.4549 | - |
570
+ | 4.7074 | 106980 | 0.4767 | - |
571
+ | 4.7078 | 106990 | 0.4794 | - |
572
+ | 4.7083 | 107000 | 0.4979 | - |
573
+ | 4.7087 | 107010 | 0.4739 | - |
574
+ | 4.7091 | 107020 | 0.4941 | - |
575
+ | 4.7096 | 107030 | 0.4783 | - |
576
+ | 4.7100 | 107040 | 0.5039 | - |
577
+ | 4.7105 | 107050 | 0.4601 | - |
578
+ | 4.7109 | 107060 | 0.4761 | - |
579
+ | 4.7113 | 107070 | 0.4695 | - |
580
+ | 4.7118 | 107080 | 0.5134 | - |
581
+ | 4.7122 | 107090 | 0.4816 | - |
582
+ | 4.7127 | 107100 | 0.4791 | - |
583
+ | 4.7131 | 107110 | 0.4601 | - |
584
+ | 4.7135 | 107120 | 0.4884 | - |
585
+ | 4.7140 | 107130 | 0.4891 | - |
586
+ | 4.7144 | 107140 | 0.4559 | - |
587
+ | 4.7149 | 107150 | 0.4439 | - |
588
+ | 4.7153 | 107160 | 0.493 | - |
589
+ | 4.7157 | 107170 | 0.4851 | - |
590
+ | 4.7162 | 107180 | 0.4774 | - |
591
+ | 4.7166 | 107190 | 0.4638 | - |
592
+ | 4.7171 | 107200 | 0.4683 | - |
593
+ | 4.7175 | 107210 | 0.4733 | - |
594
+ | 4.7179 | 107220 | 0.4859 | - |
595
+ | 4.7184 | 107230 | 0.4867 | - |
596
+ | 4.7188 | 107240 | 0.4739 | - |
597
+ | 4.7193 | 107250 | 0.4948 | - |
598
+ | 4.7197 | 107260 | 0.4621 | - |
599
+ | 4.7201 | 107270 | 0.4627 | - |
600
+ | 4.7206 | 107280 | 0.498 | - |
601
+ | 4.7210 | 107290 | 0.4614 | - |
602
+ | 4.7215 | 107300 | 0.4561 | - |
603
+ | 4.7219 | 107310 | 0.4893 | - |
604
+ | 4.7223 | 107320 | 0.4621 | - |
605
+ | 4.7228 | 107330 | 0.4722 | - |
606
+ | 4.7232 | 107340 | 0.485 | - |
607
+ | 4.7237 | 107350 | 0.4628 | - |
608
+ | 4.7241 | 107360 | 0.4807 | - |
609
+ | 4.7245 | 107370 | 0.4798 | - |
610
+ | 4.7250 | 107380 | 0.4673 | - |
611
+ | 4.7254 | 107390 | 0.4703 | - |
612
+ | 4.7259 | 107400 | 0.4956 | - |
613
+ | 4.7263 | 107410 | 0.4715 | - |
614
+ | 4.7267 | 107420 | 0.4928 | - |
615
+ | 4.7272 | 107430 | 0.4854 | - |
616
+ | 4.7276 | 107440 | 0.4781 | - |
617
+ | 4.7281 | 107450 | 0.4906 | - |
618
+ | 4.7285 | 107460 | 0.491 | - |
619
+ | 4.7289 | 107470 | 0.4766 | - |
620
+ | 4.7294 | 107480 | 0.4745 | - |
621
+ | 4.7298 | 107490 | 0.4756 | - |
622
+ | 4.7303 | 107500 | 0.4839 | - |
623
+ | 4.7307 | 107510 | 0.4492 | - |
624
+ | 4.7311 | 107520 | 0.4579 | - |
625
+ | 4.7316 | 107530 | 0.4823 | - |
626
+ | 4.7320 | 107540 | 0.4514 | - |
627
+ | 4.7325 | 107550 | 0.4595 | - |
628
+ | 4.7329 | 107560 | 0.4898 | - |
629
+ | 4.7333 | 107570 | 0.4508 | - |
630
+ | 4.7338 | 107580 | 0.49 | - |
631
+ | 4.7342 | 107590 | 0.4475 | - |
632
+ | 4.7347 | 107600 | 0.4801 | - |
633
+ | 4.7351 | 107610 | 0.4665 | - |
634
+ | 4.7355 | 107620 | 0.4769 | - |
635
+ | 4.7360 | 107630 | 0.4827 | - |
636
+ | 4.7364 | 107640 | 0.4817 | - |
637
+ | 4.7369 | 107650 | 0.4608 | - |
638
+ | 4.7373 | 107660 | 0.4681 | - |
639
+ | 4.7377 | 107670 | 0.4681 | - |
640
+ | 4.7382 | 107680 | 0.5057 | - |
641
+ | 4.7386 | 107690 | 0.4849 | - |
642
+ | 4.7391 | 107700 | 0.4793 | - |
643
+ | 4.7395 | 107710 | 0.4935 | - |
644
+ | 4.7399 | 107720 | 0.4763 | - |
645
+ | 4.7404 | 107730 | 0.4774 | - |
646
+ | 4.7408 | 107740 | 0.4883 | - |
647
+ | 4.7413 | 107750 | 0.4613 | - |
648
+ | 4.7417 | 107760 | 0.4817 | - |
649
+ | 4.7421 | 107770 | 0.4721 | - |
650
+ | 4.7426 | 107780 | 0.4681 | - |
651
+ | 4.7430 | 107790 | 0.4818 | - |
652
+ | 4.7435 | 107800 | 0.4762 | - |
653
+ | 4.7439 | 107810 | 0.496 | - |
654
+ | 4.7443 | 107820 | 0.4865 | - |
655
+ | 4.7448 | 107830 | 0.4748 | - |
656
+ | 4.7452 | 107840 | 0.4525 | - |
657
+ | 4.7457 | 107850 | 0.4783 | - |
658
+ | 4.7461 | 107860 | 0.4754 | - |
659
+ | 4.7465 | 107870 | 0.4676 | - |
660
+ | 4.7470 | 107880 | 0.4811 | - |
661
+ | 4.7474 | 107890 | 0.4932 | - |
662
+ | 4.7479 | 107900 | 0.4764 | - |
663
+ | 4.7483 | 107910 | 0.4877 | - |
664
+ | 4.7487 | 107920 | 0.4709 | - |
665
+ | 4.7492 | 107930 | 0.4633 | - |
666
+ | 4.7496 | 107940 | 0.471 | - |
667
+ | 4.7501 | 107950 | 0.4692 | - |
668
+ | 4.7505 | 107960 | 0.4549 | - |
669
+ | 4.7509 | 107970 | 0.4778 | - |
670
+ | 4.7514 | 107980 | 0.4921 | - |
671
+ | 4.7518 | 107990 | 0.4801 | - |
672
+ | 4.7523 | 108000 | 0.4662 | - |
673
+ | 4.7527 | 108010 | 0.4852 | - |
674
+ | 4.7529 | 108015 | - | 1.2617 |
675
+ | 4.7531 | 108020 | 0.4915 | - |
676
+ | 4.7536 | 108030 | 0.472 | - |
677
+ | 4.7540 | 108040 | 0.4906 | - |
678
+ | 4.7545 | 108050 | 0.4817 | - |
679
+ | 4.7549 | 108060 | 0.4724 | - |
680
+ | 4.7553 | 108070 | 0.4696 | - |
681
+ | 4.7558 | 108080 | 0.4791 | - |
682
+ | 4.7562 | 108090 | 0.4819 | - |
683
+ | 4.7567 | 108100 | 0.4953 | - |
684
+ | 4.7571 | 108110 | 0.4665 | - |
685
+ | 4.7575 | 108120 | 0.4688 | - |
686
+ | 4.7580 | 108130 | 0.4791 | - |
687
+ | 4.7584 | 108140 | 0.4734 | - |
688
+ | 4.7589 | 108150 | 0.4828 | - |
689
+ | 4.7593 | 108160 | 0.4718 | - |
690
+ | 4.7597 | 108170 | 0.4813 | - |
691
+ | 4.7602 | 108180 | 0.4827 | - |
692
+ | 4.7606 | 108190 | 0.4993 | - |
693
+ | 4.7611 | 108200 | 0.4745 | - |
694
+ | 4.7615 | 108210 | 0.4777 | - |
695
+ | 4.7619 | 108220 | 0.4757 | - |
696
+ | 4.7624 | 108230 | 0.4799 | - |
697
+ | 4.7628 | 108240 | 0.4936 | - |
698
+ | 4.7633 | 108250 | 0.4893 | - |
699
+ | 4.7637 | 108260 | 0.464 | - |
700
+ | 4.7641 | 108270 | 0.4669 | - |
701
+ | 4.7646 | 108280 | 0.4921 | - |
702
+ | 4.7650 | 108290 | 0.4815 | - |
703
+ | 4.7655 | 108300 | 0.4836 | - |
704
+ | 4.7659 | 108310 | 0.4718 | - |
705
+ | 4.7663 | 108320 | 0.4574 | - |
706
+ | 4.7668 | 108330 | 0.4779 | - |
707
+ | 4.7672 | 108340 | 0.4849 | - |
708
+ | 4.7677 | 108350 | 0.4849 | - |
709
+ | 4.7681 | 108360 | 0.4601 | - |
710
+ | 4.7685 | 108370 | 0.4654 | - |
711
+ | 4.7690 | 108380 | 0.4704 | - |
712
+ | 4.7694 | 108390 | 0.4727 | - |
713
+ | 4.7699 | 108400 | 0.48 | - |
714
+ | 4.7703 | 108410 | 0.4726 | - |
715
+ | 4.7707 | 108420 | 0.4791 | - |
716
+ | 4.7712 | 108430 | 0.4519 | - |
717
+ | 4.7716 | 108440 | 0.4568 | - |
718
+ | 4.7721 | 108450 | 0.4833 | - |
719
+ | 4.7725 | 108460 | 0.476 | - |
720
+ | 4.7729 | 108470 | 0.4597 | - |
721
+ | 4.7734 | 108480 | 0.4745 | - |
722
+ | 4.7738 | 108490 | 0.4744 | - |
723
+ | 4.7743 | 108500 | 0.4601 | - |
724
+ | 4.7747 | 108510 | 0.4807 | - |
725
+ | 4.7751 | 108520 | 0.463 | - |
726
+ | 4.7756 | 108530 | 0.4761 | - |
727
+ | 4.7760 | 108540 | 0.4716 | - |
728
+ | 4.7765 | 108550 | 0.5068 | - |
729
+ | 4.7769 | 108560 | 0.4832 | - |
730
+ | 4.7773 | 108570 | 0.4641 | - |
731
+ | 4.7778 | 108580 | 0.466 | - |
732
+ | 4.7782 | 108590 | 0.4635 | - |
733
+ | 4.7787 | 108600 | 0.5043 | - |
734
+ | 4.7791 | 108610 | 0.4563 | - |
735
+ | 4.7795 | 108620 | 0.4998 | - |
736
+ | 4.7800 | 108630 | 0.5168 | - |
737
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738
+ | 4.7809 | 108650 | 0.4658 | - |
739
+ | 4.7813 | 108660 | 0.4594 | - |
740
+ | 4.7817 | 108670 | 0.4552 | - |
741
+ | 4.7822 | 108680 | 0.4604 | - |
742
+ | 4.7826 | 108690 | 0.4742 | - |
743
+ | 4.7831 | 108700 | 0.5057 | - |
744
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745
+ | 4.7839 | 108720 | 0.4626 | - |
746
+ | 4.7844 | 108730 | 0.4581 | - |
747
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748
+ | 4.7853 | 108750 | 0.4914 | - |
749
+ | 4.7857 | 108760 | 0.4838 | - |
750
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751
+ | 4.7866 | 108780 | 0.5038 | - |
752
+ | 4.7870 | 108790 | 0.4858 | - |
753
+ | 4.7875 | 108800 | 0.4516 | - |
754
+ | 4.7879 | 108810 | 0.4685 | - |
755
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756
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757
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758
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759
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760
+ | 4.7905 | 108870 | 0.4624 | - |
761
+ | 4.7910 | 108880 | 0.4631 | - |
762
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763
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764
+ | 4.7923 | 108910 | 0.4857 | - |
765
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766
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767
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768
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769
+ | 4.7945 | 108960 | 0.4845 | - |
770
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771
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772
+ | 4.7958 | 108990 | 0.4975 | - |
773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
+ | 4.8030 | 109152 | - | 1.2595 |
790
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
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811
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812
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813
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814
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815
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816
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817
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818
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819
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820
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821
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822
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823
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824
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825
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826
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827
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828
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829
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830
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831
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832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
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850
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851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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898
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899
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
+ | 4.8667 | 110600 | 0.4815 | - |
936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
+ | 4.8772 | 110840 | 0.4876 | - |
960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
+ | 4.8843 | 111000 | 0.4973 | - |
976
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977
+ | 4.8852 | 111020 | 0.4816 | - |
978
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979
+ | 4.8860 | 111040 | 0.453 | - |
980
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981
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982
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983
+ | 4.8878 | 111080 | 0.4828 | - |
984
+ | 4.8882 | 111090 | 0.4686 | - |
985
+ | 4.8887 | 111100 | 0.4742 | - |
986
+ | 4.8891 | 111110 | 0.4558 | - |
987
+ | 4.8896 | 111120 | 0.4988 | - |
988
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989
+ | 4.8904 | 111140 | 0.4722 | - |
990
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991
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992
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993
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994
+ | 4.8926 | 111190 | 0.4575 | - |
995
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996
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997
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998
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999
+ | 4.8948 | 111240 | 0.4589 | - |
1000
+ | 4.8953 | 111250 | 0.485 | - |
1001
+ | 4.8957 | 111260 | 0.4804 | - |
1002
+ | 4.8962 | 111270 | 0.4439 | - |
1003
+ | 4.8966 | 111280 | 0.4743 | - |
1004
+ | 4.8970 | 111290 | 0.4799 | - |
1005
+ | 4.8975 | 111300 | 0.4653 | - |
1006
+ | 4.8979 | 111310 | 0.4941 | - |
1007
+ | 4.8984 | 111320 | 0.4618 | - |
1008
+ | 4.8988 | 111330 | 0.4753 | - |
1009
+ | 4.8992 | 111340 | 0.484 | - |
1010
+ | 4.8997 | 111350 | 0.4785 | - |
1011
+ | 4.9001 | 111360 | 0.4871 | - |
1012
+ | 4.9006 | 111370 | 0.4626 | - |
1013
+ | 4.9010 | 111380 | 0.4943 | - |
1014
+ | 4.9014 | 111390 | 0.4885 | - |
1015
+ | 4.9019 | 111400 | 0.4798 | - |
1016
+ | 4.9023 | 111410 | 0.4837 | - |
1017
+ | 4.9028 | 111420 | 0.4733 | - |
1018
+ | 4.9030 | 111426 | - | 1.2603 |
1019
+ | 4.9032 | 111430 | 0.4807 | - |
1020
+ | 4.9036 | 111440 | 0.4902 | - |
1021
+ | 4.9041 | 111450 | 0.4677 | - |
1022
+ | 4.9045 | 111460 | 0.4815 | - |
1023
+ | 4.9050 | 111470 | 0.4674 | - |
1024
+ | 4.9054 | 111480 | 0.4878 | - |
1025
+ | 4.9058 | 111490 | 0.4574 | - |
1026
+ | 4.9063 | 111500 | 0.4699 | - |
1027
+ | 4.9067 | 111510 | 0.484 | - |
1028
+ | 4.9072 | 111520 | 0.4876 | - |
1029
+ | 4.9076 | 111530 | 0.4758 | - |
1030
+ | 4.9080 | 111540 | 0.458 | - |
1031
+ | 4.9085 | 111550 | 0.4681 | - |
1032
+ | 4.9089 | 111560 | 0.4815 | - |
1033
+ | 4.9094 | 111570 | 0.4676 | - |
1034
+ | 4.9098 | 111580 | 0.4651 | - |
1035
+ | 4.9102 | 111590 | 0.4532 | - |
1036
+ | 4.9107 | 111600 | 0.48 | - |
1037
+ | 4.9111 | 111610 | 0.4988 | - |
1038
+ | 4.9116 | 111620 | 0.4623 | - |
1039
+ | 4.9120 | 111630 | 0.4868 | - |
1040
+ | 4.9124 | 111640 | 0.4718 | - |
1041
+ | 4.9129 | 111650 | 0.4846 | - |
1042
+ | 4.9133 | 111660 | 0.4547 | - |
1043
+ | 4.9138 | 111670 | 0.491 | - |
1044
+ | 4.9142 | 111680 | 0.4834 | - |
1045
+ | 4.9146 | 111690 | 0.4864 | - |
1046
+ | 4.9151 | 111700 | 0.4706 | - |
1047
+ | 4.9155 | 111710 | 0.4732 | - |
1048
+ | 4.9160 | 111720 | 0.4575 | - |
1049
+ | 4.9164 | 111730 | 0.4761 | - |
1050
+ | 4.9168 | 111740 | 0.4848 | - |
1051
+ | 4.9173 | 111750 | 0.4748 | - |
1052
+ | 4.9177 | 111760 | 0.4873 | - |
1053
+ | 4.9182 | 111770 | 0.4561 | - |
1054
+ | 4.9186 | 111780 | 0.4928 | - |
1055
+ | 4.9190 | 111790 | 0.4813 | - |
1056
+ | 4.9195 | 111800 | 0.4766 | - |
1057
+ | 4.9199 | 111810 | 0.4764 | - |
1058
+ | 4.9204 | 111820 | 0.4423 | - |
1059
+ | 4.9208 | 111830 | 0.4877 | - |
1060
+ | 4.9212 | 111840 | 0.4587 | - |
1061
+ | 4.9217 | 111850 | 0.4941 | - |
1062
+ | 4.9221 | 111860 | 0.4841 | - |
1063
+ | 4.9226 | 111870 | 0.4725 | - |
1064
+ | 4.9230 | 111880 | 0.501 | - |
1065
+ | 4.9234 | 111890 | 0.4562 | - |
1066
+ | 4.9239 | 111900 | 0.4752 | - |
1067
+ | 4.9243 | 111910 | 0.4876 | - |
1068
+ | 4.9248 | 111920 | 0.4877 | - |
1069
+ | 4.9252 | 111930 | 0.4803 | - |
1070
+ | 4.9256 | 111940 | 0.4617 | - |
1071
+ | 4.9261 | 111950 | 0.4801 | - |
1072
+ | 4.9265 | 111960 | 0.4807 | - |
1073
+ | 4.9270 | 111970 | 0.4769 | - |
1074
+ | 4.9274 | 111980 | 0.4793 | - |
1075
+ | 4.9278 | 111990 | 0.4845 | - |
1076
+ | 4.9283 | 112000 | 0.4903 | - |
1077
+ | 4.9287 | 112010 | 0.4665 | - |
1078
+ | 4.9292 | 112020 | 0.4654 | - |
1079
+ | 4.9296 | 112030 | 0.4741 | - |
1080
+ | 4.9300 | 112040 | 0.4635 | - |
1081
+ | 4.9305 | 112050 | 0.4757 | - |
1082
+ | 4.9309 | 112060 | 0.5063 | - |
1083
+ | 4.9314 | 112070 | 0.4591 | - |
1084
+ | 4.9318 | 112080 | 0.4725 | - |
1085
+ | 4.9322 | 112090 | 0.4821 | - |
1086
+ | 4.9327 | 112100 | 0.4732 | - |
1087
+ | 4.9331 | 112110 | 0.4484 | - |
1088
+ | 4.9336 | 112120 | 0.4517 | - |
1089
+ | 4.9340 | 112130 | 0.4764 | - |
1090
+ | 4.9344 | 112140 | 0.494 | - |
1091
+ | 4.9349 | 112150 | 0.492 | - |
1092
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1093
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1094
+ | 4.9362 | 112180 | 0.4846 | - |
1095
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1096
+ | 4.9371 | 112200 | 0.4566 | - |
1097
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1098
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1099
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1100
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1101
+ | 4.9393 | 112250 | 0.4599 | - |
1102
+ | 4.9397 | 112260 | 0.4643 | - |
1103
+ | 4.9402 | 112270 | 0.482 | - |
1104
+ | 4.9406 | 112280 | 0.4489 | - |
1105
+ | 4.9410 | 112290 | 0.4976 | - |
1106
+ | 4.9415 | 112300 | 0.458 | - |
1107
+ | 4.9419 | 112310 | 0.473 | - |
1108
+ | 4.9424 | 112320 | 0.4799 | - |
1109
+ | 4.9428 | 112330 | 0.4821 | - |
1110
+ | 4.9432 | 112340 | 0.4704 | - |
1111
+ | 4.9437 | 112350 | 0.4603 | - |
1112
+ | 4.9441 | 112360 | 0.4751 | - |
1113
+ | 4.9446 | 112370 | 0.5101 | - |
1114
+ | 4.9450 | 112380 | 0.4974 | - |
1115
+ | 4.9454 | 112390 | 0.4672 | - |
1116
+ | 4.9459 | 112400 | 0.4812 | - |
1117
+ | 4.9463 | 112410 | 0.4882 | - |
1118
+ | 4.9468 | 112420 | 0.4735 | - |
1119
+ | 4.9472 | 112430 | 0.4812 | - |
1120
+ | 4.9476 | 112440 | 0.458 | - |
1121
+ | 4.9481 | 112450 | 0.4874 | - |
1122
+ | 4.9485 | 112460 | 0.4535 | - |
1123
+ | 4.9490 | 112470 | 0.4811 | - |
1124
+ | 4.9494 | 112480 | 0.4795 | - |
1125
+ | 4.9498 | 112490 | 0.4994 | - |
1126
+ | 4.9503 | 112500 | 0.4498 | - |
1127
+ | 4.9507 | 112510 | 0.4672 | - |
1128
+ | 4.9512 | 112520 | 0.4861 | - |
1129
+ | 4.9516 | 112530 | 0.464 | - |
1130
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1131
+ | 4.9525 | 112550 | 0.4804 | - |
1132
+ | 4.9529 | 112560 | 0.4979 | - |
1133
+ | 4.9530 | 112563 | - | 1.2611 |
1134
+ | 4.9534 | 112570 | 0.4769 | - |
1135
+ | 4.9538 | 112580 | 0.4854 | - |
1136
+ | 4.9542 | 112590 | 0.4864 | - |
1137
+ | 4.9547 | 112600 | 0.5016 | - |
1138
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1139
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1140
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1141
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1142
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1143
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1156
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1157
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1158
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1160
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1161
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1162
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1163
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1165
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1166
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1168
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1170
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1184
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1186
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1187
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1188
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1189
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1190
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1191
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1192
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1193
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1194
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1195
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1196
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1197
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1198
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1199
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1200
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1201
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1202
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1203
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1204
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1205
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1206
+ | 4.9850 | 113290 | 0.4655 | - |
1207
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1208
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1209
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1210
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1211
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1212
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1213
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1214
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1215
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1216
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1217
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1218
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1219
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1220
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1221
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1222
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1223
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1224
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1225
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1226
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1227
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1228
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1229
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1230
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1231
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1232
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1233
+ | 4.9969 | 113560 | 0.4819 | - |
1234
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1235
+ | 4.9978 | 113580 | 0.4712 | - |
1236
+ | 4.9982 | 113590 | 0.4552 | - |
1237
+ | 4.9987 | 113600 | 0.4529 | - |
1238
+ | 4.9991 | 113610 | 0.467 | - |
1239
+ | 4.9996 | 113620 | 0.4618 | - |
1240
+ | 5.0 | 113630 | 0.4417 | - |
1241
+
1242
+ </details>
1243
+
1244
+ ### Framework Versions
1245
+ - Python: 3.11.8
1246
+ - Sentence Transformers: 3.1.1
1247
+ - Transformers: 4.45.1
1248
+ - PyTorch: 2.5.1.post302
1249
+ - Accelerate: 0.34.2
1250
+ - Datasets: 3.0.0
1251
+ - Tokenizers: 0.20.0
1252
+
1253
+ ## Citation
1254
+
1255
+ ### BibTeX
1256
+
1257
+ #### Sentence Transformers
1258
+ ```bibtex
1259
+ @inproceedings{reimers-2019-sentence-bert,
1260
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1261
+ author = "Reimers, Nils and Gurevych, Iryna",
1262
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1263
+ month = "11",
1264
+ year = "2019",
1265
+ publisher = "Association for Computational Linguistics",
1266
+ url = "https://arxiv.org/abs/1908.10084",
1267
+ }
1268
+ ```
1269
+
1270
+ #### MaskedCachedMultipleNegativesRankingLoss
1271
+ ```bibtex
1272
+ @misc{gao2021scaling,
1273
+ title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
1274
+ author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
1275
+ year={2021},
1276
+ eprint={2101.06983},
1277
+ archivePrefix={arXiv},
1278
+ primaryClass={cs.LG}
1279
+ }
1280
+ ```
1281
+
1282
+ <!--
1283
+ ## Glossary
1284
+
1285
+ *Clearly define terms in order to be accessible across audiences.*
1286
+ -->
1287
+
1288
+ <!--
1289
+ ## Model Card Authors
1290
+
1291
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1292
+ -->
1293
+
1294
+ <!--
1295
+ ## Model Card Contact
1296
+
1297
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1298
+ -->
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