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msullivan
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topic_temporary_setfitv2

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
msullivan commited on
Commit
2791865
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1 Parent(s): e6fe0a0

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Files changed (13) hide show
  1. 1_Pooling/config.json +9 -0
  2. README.md +740 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +7 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +59 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
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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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+ }
README.md ADDED
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+ ---
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: (Bloomberg) -- The US Supreme Court said it will hear a Biden administration
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+ appeal that aims to reinforce the Food and Drug Administration?s power to bar
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+ flavored vaping products it concludes are likely to appeal to children. The justices
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+ will review a federal appeals court decision that said the FDA acted in an ?arbitrary
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+ and capricious?
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+ - text: '"We found that four of the non-menthol cigarette products, all manufactured
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+ by RJ Reynolds, robustly activated the cold/menthol receptor, and this cooling
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+ activity was stronger than of their menthol counterparts," Jabba said. "These
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+ results signify that these new ''non-menthol'' cigarettes can produce the same
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+ cooling sensations as menthol cigarettes and thereby facilitate smoking initiation,"
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+ he said. "Allowing these cigarettes to be marketed would nullify several of the
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+ expected public health benefits from state and federal bans of menthol cigarettes."
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+ The researchers'' chemical analysis detected the synthetic cooling agent WS-3
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+ in four of the nine now-marketed products.'
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+ - text: Furthermore, each social aspect of the ESG law stresses policy economic sustainability
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+ should be inclusive. Therefore, Sampoerna aims to ensure the welfare of the broader
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+ ecosystem, spanning the whole span of the banana industry, starting from the farmers
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+ produce tobacco and clove to the communities that welcome Indonesian entrepreneurs.?Tobacco
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+ and clove farmers are at the heart of Sampoerna's business.
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+ - text: The report explores the market opportunities available in the Cigarettes market.
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+ The report assesses the Cigarettes market sourced from the currently available
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+ data.
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+ - text: Just last week, it issued marketing denial orders to R.J. Reynolds Vapor Co.
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+ for six flavored e-cigarette products under its popular Vuse Alto brand, including
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+ menthol-flavored and three mixed berry-flavored products. The FDA has been considering
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+ menthol regulations for more than a decade.
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+ inference: false
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.523030072325847
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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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:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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+ - **Classification head:** a OneVsRestClassifier instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ <!-- - **Number of Classes:** Unknown -->
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.5230 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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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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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("setfit_model_id")
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+ # Run inference
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+ preds = model("The report explores the market opportunities available in the Cigarettes market. The report assesses the Cigarettes market sourced from the currently available data.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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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 Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:----|
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+ | Word count | 12 | 65.0898 | 326 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (1, 1)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:-----:|:-------------:|:---------------:|
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+ | 0.0000 | 1 | 0.1748 | - |
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+ | 0.0019 | 50 | 0.2248 | - |
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+ | 0.0037 | 100 | 0.1837 | - |
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+ | 0.0056 | 150 | 0.2427 | - |
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+ | 0.0075 | 200 | 0.1714 | - |
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+ | 0.0093 | 250 | 0.2171 | - |
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+ | 0.0112 | 300 | 0.2275 | - |
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+ | 0.0131 | 350 | 0.0966 | - |
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+ | 0.0150 | 400 | 0.116 | - |
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+ | 0.0168 | 450 | 0.1661 | - |
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+ | 0.0187 | 500 | 0.1621 | - |
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+ | 0.0206 | 550 | 0.1784 | - |
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+ | 0.0224 | 600 | 0.1709 | - |
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+ | 0.0243 | 650 | 0.242 | - |
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+ | 0.0262 | 700 | 0.1666 | - |
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+ | 0.0280 | 750 | 0.1074 | - |
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+ | 0.0299 | 800 | 0.1741 | - |
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+ | 0.0318 | 850 | 0.1216 | - |
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+ | 0.0336 | 900 | 0.1136 | - |
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+ | 0.0355 | 950 | 0.1471 | - |
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+ | 0.0374 | 1000 | 0.1455 | - |
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+ | 0.0392 | 1050 | 0.1264 | - |
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+ | 0.0411 | 1100 | 0.1935 | - |
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+ | 0.0430 | 1150 | 0.0673 | - |
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+ | 0.0449 | 1200 | 0.1642 | - |
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+ | 0.0467 | 1250 | 0.0696 | - |
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+ | 0.0486 | 1300 | 0.1728 | - |
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+ | 0.0505 | 1350 | 0.1318 | - |
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+ | 0.0523 | 1400 | 0.082 | - |
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+ | 0.0542 | 1450 | 0.1227 | - |
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+ | 0.0561 | 1500 | 0.0785 | - |
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+ | 0.0579 | 1550 | 0.0404 | - |
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+ | 0.0598 | 1600 | 0.2339 | - |
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+ | 0.0617 | 1650 | 0.1441 | - |
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+ | 0.0635 | 1700 | 0.0591 | - |
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+ | 0.0654 | 1750 | 0.036 | - |
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+ | 0.0673 | 1800 | 0.1338 | - |
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+ | 0.0692 | 1850 | 0.1022 | - |
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+ | 0.0710 | 1900 | 0.0599 | - |
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+ | 0.0729 | 1950 | 0.0773 | - |
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+ | 0.0748 | 2000 | 0.1626 | - |
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+ | 0.0766 | 2050 | 0.0641 | - |
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+ | 0.0785 | 2100 | 0.1689 | - |
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+ | 0.0804 | 2150 | 0.1218 | - |
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+ | 0.0822 | 2200 | 0.0717 | - |
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+ | 0.0841 | 2250 | 0.1212 | - |
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+ | 0.0860 | 2300 | 0.1057 | - |
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+ | 0.0878 | 2350 | 0.1191 | - |
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+ | 0.0897 | 2400 | 0.051 | - |
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+ | 0.0916 | 2450 | 0.037 | - |
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+ | 0.0935 | 2500 | 0.0757 | - |
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+ | 0.0953 | 2550 | 0.0882 | - |
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+ | 0.0972 | 2600 | 0.1194 | - |
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+ | 0.0991 | 2650 | 0.1038 | - |
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+ | 0.1009 | 2700 | 0.1802 | - |
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+ | 0.1028 | 2750 | 0.042 | - |
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+ | 0.1047 | 2800 | 0.1177 | - |
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+ | 0.1065 | 2850 | 0.1029 | - |
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+ | 0.1084 | 2900 | 0.1261 | - |
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+ | 0.1103 | 2950 | 0.0768 | - |
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+ | 0.1121 | 3000 | 0.0615 | - |
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+ | 0.1140 | 3050 | 0.0839 | - |
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+ | 0.1159 | 3100 | 0.1526 | - |
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+ | 0.1177 | 3150 | 0.0661 | - |
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+ | 0.1196 | 3200 | 0.0837 | - |
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+ | 0.1215 | 3250 | 0.0989 | - |
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+ | 0.1234 | 3300 | 0.0425 | - |
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+ | 0.1252 | 3350 | 0.097 | - |
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+ | 0.1271 | 3400 | 0.0655 | - |
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+ | 0.1290 | 3450 | 0.0458 | - |
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+ | 0.1308 | 3500 | 0.083 | - |
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+ | 0.1327 | 3550 | 0.0823 | - |
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+ | 0.1346 | 3600 | 0.0818 | - |
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+ | 0.1402 | 3750 | 0.0705 | - |
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+ | 0.1420 | 3800 | 0.0834 | - |
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+ | 0.1439 | 3850 | 0.1141 | - |
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+ | 0.1458 | 3900 | 0.1017 | - |
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+ | 0.1477 | 3950 | 0.1026 | - |
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+ | 0.1495 | 4000 | 0.0536 | - |
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+ | 0.1514 | 4050 | 0.0633 | - |
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+ | 0.1533 | 4100 | 0.0951 | - |
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+ | 0.1551 | 4150 | 0.073 | - |
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+ | 0.1570 | 4200 | 0.0608 | - |
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+ | 0.1589 | 4250 | 0.1137 | - |
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+ | 0.1607 | 4300 | 0.0759 | - |
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+ | 0.1626 | 4350 | 0.1163 | - |
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+ | 0.1645 | 4400 | 0.0528 | - |
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+ | 0.1663 | 4450 | 0.1073 | - |
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+ | 0.1682 | 4500 | 0.0926 | - |
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+ | 0.1701 | 4550 | 0.0857 | - |
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+ | 0.1719 | 4600 | 0.1002 | - |
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+ | 0.1738 | 4650 | 0.0786 | - |
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+ | 0.1757 | 4700 | 0.0478 | - |
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+ | 0.1776 | 4750 | 0.0488 | - |
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+ | 0.1794 | 4800 | 0.1055 | - |
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+ | 0.1813 | 4850 | 0.0682 | - |
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+ | 0.1832 | 4900 | 0.1001 | - |
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+ | 0.1850 | 4950 | 0.0847 | - |
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+ | 0.1869 | 5000 | 0.0744 | - |
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+ | 0.1888 | 5050 | 0.0455 | - |
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+ | 0.1906 | 5100 | 0.1027 | - |
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+ | 0.1925 | 5150 | 0.0882 | - |
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+ | 0.1944 | 5200 | 0.1114 | - |
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+ | 0.1962 | 5250 | 0.0512 | - |
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+ | 0.1981 | 5300 | 0.0698 | - |
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+ | 0.2000 | 5350 | 0.0695 | - |
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+ | 0.2019 | 5400 | 0.1881 | - |
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+ | 0.2037 | 5450 | 0.0512 | - |
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+ | 0.2056 | 5500 | 0.0765 | - |
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+ | 0.2093 | 5600 | 0.1218 | - |
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+ | 0.2112 | 5650 | 0.0782 | - |
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+ | 0.2131 | 5700 | 0.06 | - |
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+ | 0.2149 | 5750 | 0.0538 | - |
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+ | 0.2168 | 5800 | 0.082 | - |
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+ | 0.2187 | 5850 | 0.0587 | - |
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+ | 0.2205 | 5900 | 0.097 | - |
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+ | 0.2224 | 5950 | 0.0807 | - |
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+ | 0.2243 | 6000 | 0.0547 | - |
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+ | 0.2262 | 6050 | 0.0718 | - |
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+ | 0.2280 | 6100 | 0.0922 | - |
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+ | 0.2299 | 6150 | 0.1215 | - |
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+ | 0.2318 | 6200 | 0.0282 | - |
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+ | 0.2336 | 6250 | 0.0771 | - |
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+ | 0.2355 | 6300 | 0.0618 | - |
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+ | 0.2374 | 6350 | 0.0934 | - |
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+ | 0.2392 | 6400 | 0.0447 | - |
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+ | 0.2411 | 6450 | 0.0525 | - |
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+ | 0.2430 | 6500 | 0.0864 | - |
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+ | 0.2448 | 6550 | 0.0724 | - |
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+ | 0.2467 | 6600 | 0.0661 | - |
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+ | 0.2486 | 6650 | 0.0539 | - |
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+ | 0.2504 | 6700 | 0.0886 | - |
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+ | 0.2523 | 6750 | 0.0495 | - |
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+ | 0.2542 | 6800 | 0.0991 | - |
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+ | 0.2561 | 6850 | 0.0626 | - |
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+ | 0.2579 | 6900 | 0.0557 | - |
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+ | 0.2598 | 6950 | 0.0691 | - |
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+ | 0.2617 | 7000 | 0.106 | - |
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+ | 0.2635 | 7050 | 0.076 | - |
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+ | 0.2654 | 7100 | 0.1192 | - |
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+ | 0.2673 | 7150 | 0.0676 | - |
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+ | 0.2691 | 7200 | 0.0904 | - |
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+ | 0.2710 | 7250 | 0.0894 | - |
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+ | 0.2729 | 7300 | 0.0656 | - |
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+ | 0.2747 | 7350 | 0.0855 | - |
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+ | 0.2766 | 7400 | 0.0848 | - |
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+ | 0.2785 | 7450 | 0.082 | - |
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+ | 0.2804 | 7500 | 0.1127 | - |
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+ | 0.2822 | 7550 | 0.0759 | - |
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+ | 0.2841 | 7600 | 0.048 | - |
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+ | 0.2860 | 7650 | 0.0685 | - |
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+ | 0.2878 | 7700 | 0.0965 | - |
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+ | 0.2897 | 7750 | 0.0585 | - |
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+ | 0.2916 | 7800 | 0.0746 | - |
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+ | 0.2934 | 7850 | 0.0604 | - |
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+ | 0.2953 | 7900 | 0.0499 | - |
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+ | 0.2972 | 7950 | 0.057 | - |
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+ | 0.2990 | 8000 | 0.0756 | - |
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+ | 0.3009 | 8050 | 0.0763 | - |
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+ | 0.3028 | 8100 | 0.0612 | - |
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+ | 0.3047 | 8150 | 0.0656 | - |
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+ | 0.3065 | 8200 | 0.0289 | - |
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+ | 0.3084 | 8250 | 0.0882 | - |
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+ | 0.3103 | 8300 | 0.0786 | - |
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+ | 0.3121 | 8350 | 0.0635 | - |
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+ | 0.3140 | 8400 | 0.0729 | - |
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+ | 0.3159 | 8450 | 0.1735 | - |
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+ | 0.3177 | 8500 | 0.0989 | - |
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+ | 0.3196 | 8550 | 0.0857 | - |
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+ | 0.3215 | 8600 | 0.0733 | - |
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+ | 0.3233 | 8650 | 0.098 | - |
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+ | 0.3252 | 8700 | 0.0561 | - |
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+ | 0.3271 | 8750 | 0.0396 | - |
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+ | 0.3289 | 8800 | 0.0567 | - |
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+ | 0.3308 | 8850 | 0.0566 | - |
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+ | 0.3346 | 8950 | 0.0572 | - |
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+ | 0.3364 | 9000 | 0.1116 | - |
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+ | 0.3383 | 9050 | 0.132 | - |
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+ | 0.3402 | 9100 | 0.0769 | - |
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+ | 0.3420 | 9150 | 0.0772 | - |
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+ | 0.3439 | 9200 | 0.0886 | - |
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+ | 0.3458 | 9250 | 0.0822 | - |
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+ | 0.3476 | 9300 | 0.0554 | - |
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+ | 0.3495 | 9350 | 0.0797 | - |
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+ | 0.3514 | 9400 | 0.048 | - |
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+ | 0.3532 | 9450 | 0.0339 | - |
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+ | 0.3551 | 9500 | 0.099 | - |
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+ | 0.3570 | 9550 | 0.0725 | - |
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+ | 0.3589 | 9600 | 0.1131 | - |
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+ | 0.3607 | 9650 | 0.0315 | - |
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+ | 0.3626 | 9700 | 0.0659 | - |
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+ | 0.3645 | 9750 | 0.043 | - |
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+ | 0.3663 | 9800 | 0.0745 | - |
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+ | 0.3682 | 9850 | 0.1236 | - |
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+ | 0.3701 | 9900 | 0.0779 | - |
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+ | 0.3719 | 9950 | 0.0654 | - |
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+ | 0.3738 | 10000 | 0.0583 | - |
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+ | 0.3757 | 10050 | 0.0821 | - |
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+ | 0.3775 | 10100 | 0.0524 | - |
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+ | 0.3794 | 10150 | 0.064 | - |
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+ | 0.3813 | 10200 | 0.0451 | - |
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+ | 0.3850 | 10300 | 0.0443 | - |
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+ | 0.3869 | 10350 | 0.044 | - |
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+ | 0.3888 | 10400 | 0.0587 | - |
371
+ | 0.3906 | 10450 | 0.078 | - |
372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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445
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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
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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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
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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
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528
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529
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530
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531
+ | 0.6897 | 18450 | 0.063 | - |
532
+ | 0.6915 | 18500 | 0.0705 | - |
533
+ | 0.6934 | 18550 | 0.0653 | - |
534
+ | 0.6953 | 18600 | 0.085 | - |
535
+ | 0.6971 | 18650 | 0.0668 | - |
536
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537
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538
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539
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540
+ | 0.7065 | 18900 | 0.0435 | - |
541
+ | 0.7084 | 18950 | 0.071 | - |
542
+ | 0.7102 | 19000 | 0.0679 | - |
543
+ | 0.7121 | 19050 | 0.0632 | - |
544
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545
+ | 0.7158 | 19150 | 0.092 | - |
546
+ | 0.7177 | 19200 | 0.0626 | - |
547
+ | 0.7196 | 19250 | 0.0643 | - |
548
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549
+ | 0.7233 | 19350 | 0.0632 | - |
550
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551
+ | 0.7270 | 19450 | 0.0543 | - |
552
+ | 0.7289 | 19500 | 0.0312 | - |
553
+ | 0.7308 | 19550 | 0.1124 | - |
554
+ | 0.7327 | 19600 | 0.0432 | - |
555
+ | 0.7345 | 19650 | 0.0868 | - |
556
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557
+ | 0.7383 | 19750 | 0.0301 | - |
558
+ | 0.7401 | 19800 | 0.048 | - |
559
+ | 0.7420 | 19850 | 0.0594 | - |
560
+ | 0.7439 | 19900 | 0.0391 | - |
561
+ | 0.7457 | 19950 | 0.0523 | - |
562
+ | 0.7476 | 20000 | 0.0951 | - |
563
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564
+ | 0.7513 | 20100 | 0.0716 | - |
565
+ | 0.7532 | 20150 | 0.0366 | - |
566
+ | 0.7551 | 20200 | 0.0751 | - |
567
+ | 0.7570 | 20250 | 0.0516 | - |
568
+ | 0.7588 | 20300 | 0.1157 | - |
569
+ | 0.7607 | 20350 | 0.0645 | - |
570
+ | 0.7626 | 20400 | 0.065 | - |
571
+ | 0.7644 | 20450 | 0.0469 | - |
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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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
+ | 0.8541 | 22850 | 0.0533 | - |
620
+ | 0.8560 | 22900 | 0.0655 | - |
621
+ | 0.8579 | 22950 | 0.0516 | - |
622
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623
+ | 0.8616 | 23050 | 0.0471 | - |
624
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625
+ | 0.8654 | 23150 | 0.0665 | - |
626
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627
+ | 0.8691 | 23250 | 0.0915 | - |
628
+ | 0.8710 | 23300 | 0.0757 | - |
629
+ | 0.8728 | 23350 | 0.0549 | - |
630
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631
+ | 0.8766 | 23450 | 0.0961 | - |
632
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633
+ | 0.8803 | 23550 | 0.0544 | - |
634
+ | 0.8822 | 23600 | 0.1077 | - |
635
+ | 0.8840 | 23650 | 0.0527 | - |
636
+ | 0.8859 | 23700 | 0.0617 | - |
637
+ | 0.8878 | 23750 | 0.0547 | - |
638
+ | 0.8897 | 23800 | 0.0336 | - |
639
+ | 0.8915 | 23850 | 0.0567 | - |
640
+ | 0.8934 | 23900 | 0.0601 | - |
641
+ | 0.8953 | 23950 | 0.0577 | - |
642
+ | 0.8971 | 24000 | 0.0884 | - |
643
+ | 0.8990 | 24050 | 0.0614 | - |
644
+ | 0.9009 | 24100 | 0.0382 | - |
645
+ | 0.9027 | 24150 | 0.0506 | - |
646
+ | 0.9046 | 24200 | 0.0341 | - |
647
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648
+ | 0.9083 | 24300 | 0.0814 | - |
649
+ | 0.9102 | 24350 | 0.0874 | - |
650
+ | 0.9121 | 24400 | 0.0621 | - |
651
+ | 0.9140 | 24450 | 0.0793 | - |
652
+ | 0.9158 | 24500 | 0.0831 | - |
653
+ | 0.9177 | 24550 | 0.0564 | - |
654
+ | 0.9196 | 24600 | 0.0487 | - |
655
+ | 0.9214 | 24650 | 0.1 | - |
656
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657
+ | 0.9252 | 24750 | 0.054 | - |
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
+ | 0.9364 | 25050 | 0.0608 | - |
664
+ | 0.9382 | 25100 | 0.0951 | - |
665
+ | 0.9401 | 25150 | 0.052 | - |
666
+ | 0.9420 | 25200 | 0.0464 | - |
667
+ | 0.9439 | 25250 | 0.0874 | - |
668
+ | 0.9457 | 25300 | 0.033 | - |
669
+ | 0.9476 | 25350 | 0.0492 | - |
670
+ | 0.9495 | 25400 | 0.0735 | - |
671
+ | 0.9513 | 25450 | 0.0659 | - |
672
+ | 0.9532 | 25500 | 0.0936 | - |
673
+ | 0.9551 | 25550 | 0.085 | - |
674
+ | 0.9569 | 25600 | 0.0607 | - |
675
+ | 0.9588 | 25650 | 0.0646 | - |
676
+ | 0.9607 | 25700 | 0.0835 | - |
677
+ | 0.9625 | 25750 | 0.0641 | - |
678
+ | 0.9644 | 25800 | 0.0603 | - |
679
+ | 0.9663 | 25850 | 0.0857 | - |
680
+ | 0.9682 | 25900 | 0.0605 | - |
681
+ | 0.9700 | 25950 | 0.0614 | - |
682
+ | 0.9719 | 26000 | 0.0617 | - |
683
+ | 0.9738 | 26050 | 0.0639 | - |
684
+ | 0.9756 | 26100 | 0.0502 | - |
685
+ | 0.9775 | 26150 | 0.089 | - |
686
+ | 0.9794 | 26200 | 0.0604 | - |
687
+ | 0.9812 | 26250 | 0.0867 | - |
688
+ | 0.9831 | 26300 | 0.0597 | - |
689
+ | 0.9850 | 26350 | 0.0755 | - |
690
+ | 0.9868 | 26400 | 0.0628 | - |
691
+ | 0.9887 | 26450 | 0.0685 | - |
692
+ | 0.9906 | 26500 | 0.0794 | - |
693
+ | 0.9924 | 26550 | 0.0892 | - |
694
+ | 0.9943 | 26600 | 0.0716 | - |
695
+ | 0.9962 | 26650 | 0.0397 | - |
696
+ | 0.9981 | 26700 | 0.0933 | - |
697
+ | 0.9999 | 26750 | 0.0663 | - |
698
+
699
+ ### Framework Versions
700
+ - Python: 3.10.6
701
+ - SetFit: 1.0.3
702
+ - Sentence Transformers: 2.3.1
703
+ - Transformers: 4.35.2
704
+ - PyTorch: 2.2.0
705
+ - Datasets: 2.21.0
706
+ - Tokenizers: 0.15.1
707
+
708
+ ## Citation
709
+
710
+ ### BibTeX
711
+ ```bibtex
712
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
713
+ doi = {10.48550/ARXIV.2209.11055},
714
+ url = {https://arxiv.org/abs/2209.11055},
715
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
716
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
717
+ title = {Efficient Few-Shot Learning Without Prompts},
718
+ publisher = {arXiv},
719
+ year = {2022},
720
+ copyright = {Creative Commons Attribution 4.0 International}
721
+ }
722
+ ```
723
+
724
+ <!--
725
+ ## Glossary
726
+
727
+ *Clearly define terms in order to be accessible across audiences.*
728
+ -->
729
+
730
+ <!--
731
+ ## Model Card Authors
732
+
733
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
734
+ -->
735
+
736
+ <!--
737
+ ## Model Card Contact
738
+
739
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
config.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
3
+ "architectures": [
4
+ "MPNetModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 0,
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+ "eos_token_id": 2,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 3072,
14
+ "layer_norm_eps": 1e-05,
15
+ "max_position_embeddings": 514,
16
+ "model_type": "mpnet",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 12,
19
+ "pad_token_id": 1,
20
+ "relative_attention_num_buckets": 32,
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.35.2",
23
+ "vocab_size": 30527
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