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dbourget
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pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9504
loss:TripletLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
dbourget commited on
Commit
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1 Parent(s): cbb8e81

Add new SentenceTransformer model.

Browse files
Files changed (10) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +725 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +44 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +66 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 1024,
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+ "pooling_mode_cls_token": true,
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+ "pooling_mode_mean_tokens": false,
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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
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+ ---
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+ base_model: dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1
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+ library_name: sentence-transformers
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+ metrics:
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+ - cosine_accuracy
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+ - dot_accuracy
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+ - manhattan_accuracy
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+ - euclidean_accuracy
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+ - max_accuracy
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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:9504
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+ - loss:TripletLoss
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+ widget:
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+ - source_sentence: cap product
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+ sentences:
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+ - method of adjoining a chain of degree p with a co-chain of degree q, where q is
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+ less than or equal to p, to form a composite chain of degree p-q
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+ - 'Ontology '
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+ - hat commodity
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+ - source_sentence: cognitivism
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+ sentences:
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+ - supporting cognitive science
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+ - study of changes in organisms caused by modification of gene expression rather
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+ than alteration of the genetic code
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+ - 'the idea that mind works like an algorithmic symbol manipulation '
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+ - source_sentence: doxastic voluntarism
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+ sentences:
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+ - Land surrounded by water
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+ - belief one is free
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+ - the ability to will beliefs
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+ - source_sentence: conceptual role
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+ sentences:
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+ - concept
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+ - inferential role
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+ - 'Theory of knowledge '
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+ - source_sentence: scientific revolutions
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+ sentences:
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+ - scientific realism
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+ - Universal moral principles govern legal systems
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+ - paradigm shifts
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+ model-index:
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+ - name: SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1
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+ results:
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+ - task:
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+ type: triplet
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+ name: Triplet
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+ dataset:
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+ name: beatai dev
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+ type: beatai-dev
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+ metrics:
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+ - type: cosine_accuracy
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+ value: 0.7929292929292929
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+ name: Cosine Accuracy
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+ - type: dot_accuracy
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+ value: 0.2542087542087542
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+ name: Dot Accuracy
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+ - type: manhattan_accuracy
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+ value: 0.8021885521885522
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+ name: Manhattan Accuracy
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+ - type: euclidean_accuracy
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+ value: 0.8013468013468014
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+ name: Euclidean Accuracy
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+ - type: max_accuracy
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+ value: 0.8021885521885522
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+ name: Max Accuracy
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+ ---
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+
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+ # SentenceTransformer based on dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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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:** [dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1](https://huggingface.co/dbourget/pb-small-10e-tsdae6e-philsim-cosine-3e-pt1) <!-- at revision e3be09e156ca8e2b7b4e5d296fc50a316393eda3 -->
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Output Dimensionality:** 1024 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: BertModel
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+ (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
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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("dbourget/pb-small-10e-tsdae6e-philsim-cosine-6e-beatai-cosine-50e")
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+ # Run inference
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+ sentences = [
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+ 'scientific revolutions',
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+ 'paradigm shifts',
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+ 'scientific realism',
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+ ]
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+ embeddings = model.encode(sentences)
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+ print(embeddings.shape)
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+ # [3, 1024]
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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]
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+ ```
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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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+ ## Evaluation
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+
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+ ### Metrics
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+
164
+ #### Triplet
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+ * Dataset: `beatai-dev`
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+ * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | **cosine_accuracy** | **0.7929** |
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+ | dot_accuracy | 0.2542 |
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+ | manhattan_accuracy | 0.8022 |
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+ | euclidean_accuracy | 0.8013 |
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+ | max_accuracy | 0.8022 |
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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 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`: 138
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+ - `per_device_eval_batch_size`: 138
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+ - `learning_rate`: 5e-07
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+ - `weight_decay`: 0.01
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+ - `num_train_epochs`: 50
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+ - `lr_scheduler_type`: constant
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+ - `bf16`: True
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+ - `dataloader_drop_last`: True
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+ - `resume_from_checkpoint`: True
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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`: 138
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+ - `per_device_eval_batch_size`: 138
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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`: 5e-07
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+ - `weight_decay`: 0.01
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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`: 50
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: constant
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0
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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`: True
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: 2
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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`: True
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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
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+
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+ </details>
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+
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+ ### Training Logs
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+ <details><summary>Click to expand</summary>
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+
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+ | Epoch | Step | Training Loss | loss | beatai-dev_cosine_accuracy |
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+ |:-------:|:----:|:-------------:|:------:|:--------------------------:|
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+ | 0 | 0 | - | - | 0.4764 |
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+ | 0.1471 | 10 | 0.2061 | - | - |
329
+ | 0.2941 | 20 | 0.2048 | - | - |
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+ | 0.4412 | 30 | 0.204 | - | - |
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+ | 0.5882 | 40 | 0.202 | - | - |
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+ | 0.7353 | 50 | 0.2019 | 0.2010 | 0.5219 |
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+ | 0.8824 | 60 | 0.2017 | - | - |
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+ | 1.0294 | 70 | 0.1954 | - | - |
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+ | 1.1765 | 80 | 0.1959 | - | - |
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+ | 1.3235 | 90 | 0.1941 | - | - |
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+ | 1.4706 | 100 | 0.1937 | 0.1929 | 0.5598 |
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+ | 1.6176 | 110 | 0.1923 | - | - |
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+ | 1.7647 | 120 | 0.1893 | - | - |
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+ | 1.9118 | 130 | 0.1861 | - | - |
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+ | 2.0588 | 140 | 0.1842 | - | - |
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+ | 2.2059 | 150 | 0.1818 | 0.1814 | 0.5985 |
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+ | 2.3529 | 160 | 0.1834 | - | - |
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+ | 2.5 | 170 | 0.1729 | - | - |
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+ | 2.6471 | 180 | 0.1726 | - | - |
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+ | 2.7941 | 190 | 0.1668 | - | - |
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+ | 2.9412 | 200 | 0.1622 | 0.1653 | 0.6330 |
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+ | 3.0882 | 210 | 0.1604 | - | - |
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+ | 3.2353 | 220 | 0.1572 | - | - |
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+ | 3.3824 | 230 | 0.159 | - | - |
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+ | 3.5294 | 240 | 0.1567 | - | - |
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+ | 3.6765 | 250 | 0.1481 | 0.1562 | 0.6532 |
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+ | 3.8235 | 260 | 0.148 | - | - |
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+ | 3.9706 | 270 | 0.1492 | - | - |
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+ | 4.1176 | 280 | 0.1528 | - | - |
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+ | 4.2647 | 290 | 0.1437 | - | - |
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+ | 4.4118 | 300 | 0.1481 | 0.1490 | 0.6658 |
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+ | 4.5588 | 310 | 0.1386 | - | - |
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+ | 4.7059 | 320 | 0.1413 | - | - |
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+ | 4.8529 | 330 | 0.1407 | - | - |
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+ | 5.0 | 340 | 0.1387 | - | - |
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+ | 5.1471 | 350 | 0.1423 | 0.1438 | 0.6717 |
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+ | 5.2941 | 360 | 0.1376 | - | - |
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+ | 5.4412 | 370 | 0.1314 | - | - |
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+ | 5.5882 | 380 | 0.1416 | - | - |
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+ | 5.7353 | 390 | 0.1284 | - | - |
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+ | 5.8824 | 400 | 0.1375 | 0.1394 | 0.6801 |
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+ | 6.0294 | 410 | 0.1308 | - | - |
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+ | 6.1765 | 420 | 0.1286 | - | - |
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+ | 6.3235 | 430 | 0.1326 | - | - |
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+ | 6.4706 | 440 | 0.1356 | - | - |
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+ | 6.6176 | 450 | 0.1298 | 0.1361 | 0.6877 |
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+ | 6.7647 | 460 | 0.1242 | - | - |
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+ | 6.9118 | 470 | 0.1299 | - | - |
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+ | 7.0588 | 480 | 0.1279 | - | - |
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+ | 7.2059 | 490 | 0.1234 | - | - |
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+ | 7.3529 | 500 | 0.1298 | 0.1333 | 0.7045 |
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+ | 7.5 | 510 | 0.1252 | - | - |
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+ | 7.6471 | 520 | 0.1248 | - | - |
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+ | 7.7941 | 530 | 0.1241 | - | - |
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+ | 7.9412 | 540 | 0.126 | - | - |
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+ | 8.0882 | 550 | 0.1252 | 0.1316 | 0.7071 |
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+ | 8.2353 | 560 | 0.1237 | - | - |
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+ | 8.3824 | 570 | 0.1205 | - | - |
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+ | 8.5294 | 580 | 0.1195 | - | - |
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+ | 8.6765 | 590 | 0.1187 | - | - |
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+ | 8.8235 | 600 | 0.1187 | 0.1293 | 0.7138 |
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+ | 8.9706 | 610 | 0.1269 | - | - |
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+ | 9.1176 | 620 | 0.1261 | - | - |
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+ | 9.2647 | 630 | 0.1182 | - | - |
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+ | 9.4118 | 640 | 0.1219 | - | - |
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+ | 9.5588 | 650 | 0.1173 | 0.1276 | 0.7172 |
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+ | 9.7059 | 660 | 0.1182 | - | - |
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+ | 9.8529 | 670 | 0.122 | - | - |
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+ | 10.0 | 680 | 0.1179 | - | - |
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+ | 10.1471 | 690 | 0.1137 | - | - |
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+ | 10.2941 | 700 | 0.1248 | 0.1261 | 0.7247 |
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+ | 10.4412 | 710 | 0.1162 | - | - |
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+ | 10.5882 | 720 | 0.1166 | - | - |
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+ | 10.7353 | 730 | 0.1111 | - | - |
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+ | 10.8824 | 740 | 0.115 | - | - |
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+ | 11.0294 | 750 | 0.1175 | 0.1247 | 0.7298 |
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+ | 11.1765 | 760 | 0.1136 | - | - |
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+ | 11.3235 | 770 | 0.1172 | - | - |
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+ | 11.4706 | 780 | 0.1158 | - | - |
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+ | 11.6176 | 790 | 0.1142 | - | - |
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+ | 11.7647 | 800 | 0.1097 | 0.1236 | 0.7332 |
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+ | 11.9118 | 810 | 0.1161 | - | - |
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+ | 12.0588 | 820 | 0.1153 | - | - |
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+ | 12.2059 | 830 | 0.1114 | - | - |
411
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412
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413
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414
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415
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416
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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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424
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425
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426
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427
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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
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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
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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
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485
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486
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487
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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
+ | 24.8529 | 1690 | 0.0916 | - | - |
497
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498
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499
+ | 25.2941 | 1720 | 0.0952 | - | - |
500
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501
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502
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503
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504
+ | 26.0294 | 1770 | 0.0931 | - | - |
505
+ | 26.1765 | 1780 | 0.0872 | - | - |
506
+ | 26.3235 | 1790 | 0.0915 | - | - |
507
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508
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509
+ | 26.7647 | 1820 | 0.0944 | - | - |
510
+ | 26.9118 | 1830 | 0.0908 | - | - |
511
+ | 27.0588 | 1840 | 0.089 | - | - |
512
+ | 27.2059 | 1850 | 0.0944 | 0.1082 | 0.7626 |
513
+ | 27.3529 | 1860 | 0.0926 | - | - |
514
+ | 27.5 | 1870 | 0.087 | - | - |
515
+ | 27.6471 | 1880 | 0.0904 | - | - |
516
+ | 27.7941 | 1890 | 0.0886 | - | - |
517
+ | 27.9412 | 1900 | 0.0942 | 0.1077 | 0.7635 |
518
+ | 28.0882 | 1910 | 0.0947 | - | - |
519
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520
+ | 28.3824 | 1930 | 0.0908 | - | - |
521
+ | 28.5294 | 1940 | 0.0943 | - | - |
522
+ | 28.6765 | 1950 | 0.0902 | 0.1071 | 0.7668 |
523
+ | 28.8235 | 1960 | 0.0909 | - | - |
524
+ | 28.9706 | 1970 | 0.0897 | - | - |
525
+ | 29.1176 | 1980 | 0.0924 | - | - |
526
+ | 29.2647 | 1990 | 0.0909 | - | - |
527
+ | 29.4118 | 2000 | 0.0895 | 0.1066 | 0.7652 |
528
+ | 29.5588 | 2010 | 0.0832 | - | - |
529
+ | 29.7059 | 2020 | 0.0883 | - | - |
530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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598
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600
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620
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621
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629
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630
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631
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632
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633
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634
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635
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636
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638
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639
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640
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642
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643
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644
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645
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646
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
+ | 49.8529 | 3390 | 0.0707 | - | - |
667
+ | 50.0 | 3400 | 0.0689 | 0.0972 | 0.7929 |
668
+
669
+ </details>
670
+
671
+ ### Framework Versions
672
+ - Python: 3.8.18
673
+ - Sentence Transformers: 3.1.1
674
+ - Transformers: 4.45.1
675
+ - PyTorch: 1.13.1+cu117
676
+ - Accelerate: 0.34.2
677
+ - Datasets: 3.0.0
678
+ - Tokenizers: 0.20.0
679
+
680
+ ## Citation
681
+
682
+ ### BibTeX
683
+
684
+ #### Sentence Transformers
685
+ ```bibtex
686
+ @inproceedings{reimers-2019-sentence-bert,
687
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
688
+ author = "Reimers, Nils and Gurevych, Iryna",
689
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
690
+ month = "11",
691
+ year = "2019",
692
+ publisher = "Association for Computational Linguistics",
693
+ url = "https://arxiv.org/abs/1908.10084",
694
+ }
695
+ ```
696
+
697
+ #### TripletLoss
698
+ ```bibtex
699
+ @misc{hermans2017defense,
700
+ title={In Defense of the Triplet Loss for Person Re-Identification},
701
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
702
+ year={2017},
703
+ eprint={1703.07737},
704
+ archivePrefix={arXiv},
705
+ primaryClass={cs.CV}
706
+ }
707
+ ```
708
+
709
+ <!--
710
+ ## Glossary
711
+
712
+ *Clearly define terms in order to be accessible across audiences.*
713
+ -->
714
+
715
+ <!--
716
+ ## Model Card Authors
717
+
718
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
719
+ -->
720
+
721
+ <!--
722
+ ## Model Card Contact
723
+
724
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
725
+ -->
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+ }
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+ }
special_tokens_map.json ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "[PAD]",
4
+ "[UNK]",
5
+ "[CLS]",
6
+ "[SEP]",
7
+ "[MASK]"
8
+ ],
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+ "cls_token": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "mask_token": {
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+ "content": "[MASK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "pad_token": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "sep_token": {
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+ "content": "[SEP]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
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+ "unk_token": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
44
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "[PAD]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "1": {
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+ "content": "[UNK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "[CLS]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "3": {
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+ "content": "[SEP]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "4": {
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+ "content": "[MASK]",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "additional_special_tokens": [
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+ "[PAD]",
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+ "[UNK]",
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+ "[CLS]",
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+ "[SEP]",
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+ "[MASK]"
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+ ],
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+ "clean_up_tokenization_spaces": true,
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+ "cls_token": "[CLS]",
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+ "mask_token": "[MASK]",
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+ "max_length": 512,
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+ "model_max_length": 512,
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+ "pad_to_multiple_of": null,
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+ "pad_token": "[PAD]",
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+ "pad_token_type_id": 0,
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+ "padding_side": "right",
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+ "sep_token": "[SEP]",
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+ "stride": 0,
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+ "tokenizer_class": "PreTrainedTokenizerFast",
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+ "truncation_side": "right",
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+ "truncation_strategy": "longest_first",
65
+ "unk_token": "[UNK]"
66
+ }