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st-karlos-efood
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setfit-multilabel-one-vs-rest-batchsize-128

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Add SetFit model

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  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +1233 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +9 -0
  6. config_setfit.json +4 -0
  7. model.safetensors +3 -0
  8. model_head.pkl +3 -0
  9. modules.json +14 -0
  10. sentence_bert_config.json +4 -0
  11. sentencepiece.bpe.model +3 -0
  12. special_tokens_map.json +51 -0
  13. tokenizer.json +3 -0
  14. tokenizer_config.json +61 -0
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ ---
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+ library_name: setfit
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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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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: boiled eggs αυγα βραστο ψωμι ροκα ντοματινια παρμεζανα
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+ - text: tomato mozza bites bites ντοματα μοτσαρελα συνοδευονται απο κετσαπ
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+ - text: φυσικος χυμος exotic ανανα lime grapefruit
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+ - text: ρυζι αρωματικο
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+ - text: βλαχικο κιλο διαμορφωθει ζυγισμα
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+ pipeline_tag: text-classification
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+ inference: false
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+ base_model: lighteternal/stsb-xlm-r-greek-transfer
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+ model-index:
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+ - name: SetFit with lighteternal/stsb-xlm-r-greek-transfer
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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: 0.16152716593245228
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with lighteternal/stsb-xlm-r-greek-transfer
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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 [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) 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:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer)
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+ - **Classification head:** a OneVsRestClassifier instance
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+ - **Maximum Sequence Length:** 400 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** | 0.1615 |
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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("st-karlos-efood/setfit-multilabel-one-vs-rest-batchsize-128")
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+ # Run inference
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+ preds = model("ρυζι αρωματικο")
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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 | 1 | 8.6228 | 116 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (32, 32)
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+ - num_epochs: (10, 10)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 25
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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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+ | 0.0002 | 1 | 0.2116 | - |
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+ | 0.0095 | 50 | 0.2101 | - |
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+ | 0.0191 | 100 | 0.201 | - |
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+ | 0.0286 | 150 | 0.208 | - |
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+ | 0.0381 | 200 | 0.2133 | - |
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+ | 0.0477 | 250 | 0.2323 | - |
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+ | 0.0572 | 300 | 0.1858 | - |
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+ | 0.0667 | 350 | 0.1705 | - |
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+ | 0.0763 | 400 | 0.1122 | - |
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+ | 0.0858 | 450 | 0.1609 | - |
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+ | 0.0953 | 500 | 0.1703 | - |
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+ | 0.1049 | 550 | 0.1898 | - |
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+ | 0.1144 | 600 | 0.133 | - |
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+ | 0.1240 | 650 | 0.1765 | - |
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+ | 0.1335 | 700 | 0.2161 | - |
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+ | 0.1430 | 750 | 0.1536 | - |
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+ | 0.1526 | 800 | 0.1675 | - |
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+ | 0.1621 | 850 | 0.0806 | - |
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+ | 0.1716 | 900 | 0.1375 | - |
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+ | 0.1812 | 950 | 0.1162 | - |
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+ | 0.1907 | 1000 | 0.0983 | - |
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+ | 0.2002 | 1050 | 0.1205 | - |
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+ | 0.2098 | 1100 | 0.127 | - |
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+ | 0.2193 | 1150 | 0.1605 | - |
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+ | 0.2288 | 1200 | 0.1583 | - |
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+ | 0.2384 | 1250 | 0.0667 | - |
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+ | 0.2479 | 1300 | 0.1441 | - |
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+ | 0.2574 | 1350 | 0.1317 | - |
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+ | 0.2670 | 1400 | 0.0621 | - |
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+ | 0.2765 | 1450 | 0.2162 | - |
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+ | 0.2860 | 1500 | 0.0682 | - |
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+ | 0.2956 | 1550 | 0.0699 | - |
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+ | 0.3051 | 1600 | 0.1266 | - |
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+ | 0.3146 | 1650 | 0.141 | - |
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+ | 0.3242 | 1700 | 0.0676 | - |
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+ | 0.3337 | 1750 | 0.1587 | - |
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+ | 0.3432 | 1800 | 0.1613 | - |
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+ | 0.3528 | 1850 | 0.1283 | - |
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+ | 0.3623 | 1900 | 0.0748 | - |
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+ | 0.3719 | 1950 | 0.1733 | - |
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+ | 0.3814 | 2000 | 0.1166 | - |
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+ | 0.3909 | 2050 | 0.11 | - |
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+ | 0.4005 | 2100 | 0.0898 | - |
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+ | 0.4100 | 2150 | 0.0862 | - |
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+ | 0.4195 | 2200 | 0.0652 | - |
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+ | 0.4291 | 2250 | 0.1117 | - |
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+ | 0.4386 | 2300 | 0.0926 | - |
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+ | 0.4481 | 2350 | 0.1522 | - |
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+ | 0.4577 | 2400 | 0.095 | - |
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+ | 0.4672 | 2450 | 0.1358 | - |
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+ | 0.4767 | 2500 | 0.119 | - |
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+ | 0.4863 | 2550 | 0.1214 | - |
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+ | 0.4958 | 2600 | 0.1127 | - |
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+ | 0.5053 | 2650 | 0.1516 | - |
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+ | 0.5149 | 2700 | 0.1123 | - |
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+ | 0.5244 | 2750 | 0.1196 | - |
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+ | 0.5339 | 2800 | 0.0625 | - |
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+ | 0.5435 | 2850 | 0.1311 | - |
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+ | 0.5530 | 2900 | 0.1399 | - |
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+ | 0.5625 | 2950 | 0.1505 | - |
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+ | 0.5721 | 3000 | 0.0909 | - |
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+ | 0.5816 | 3050 | 0.1858 | - |
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+ | 0.5912 | 3100 | 0.0587 | - |
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+ | 0.6388 | 3350 | 0.0804 | - |
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+ | 0.6579 | 3450 | 0.0883 | - |
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+ | 0.6674 | 3500 | 0.0787 | - |
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+ | 0.6865 | 3600 | 0.0993 | - |
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+ | 0.6960 | 3650 | 0.1896 | - |
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+ | 0.7056 | 3700 | 0.1316 | - |
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+ | 0.7151 | 3750 | 0.0738 | - |
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+ | 0.7246 | 3800 | 0.0885 | - |
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+ | 0.7342 | 3850 | 0.0848 | - |
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+ | 0.7437 | 3900 | 0.1026 | - |
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+ | 0.7532 | 3950 | 0.1176 | - |
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+ | 0.7628 | 4000 | 0.0822 | - |
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+ | 0.7723 | 4050 | 0.1107 | - |
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+ | 0.7818 | 4100 | 0.0975 | - |
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+ | 0.7914 | 4150 | 0.116 | - |
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+ | 0.8009 | 4200 | 0.1212 | - |
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+ | 0.8105 | 4250 | 0.0757 | - |
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+ | 1.0393 | 5450 | 0.1948 | - |
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+ | 1.0870 | 5700 | 0.1605 | - |
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+ | 1.1156 | 5850 | 0.0287 | - |
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+ | 1.1251 | 5900 | 0.077 | - |
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+ | 1.1346 | 5950 | 0.1137 | - |
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+ | 1.1442 | 6000 | 0.1729 | - |
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+ | 1.1728 | 6150 | 0.0792 | - |
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+ | 1.1823 | 6200 | 0.1213 | - |
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+ | 1.1918 | 6250 | 0.079 | - |
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+ | 1.2014 | 6300 | 0.1397 | - |
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+ | 1.2109 | 6350 | 0.0609 | - |
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+ | 1.2204 | 6400 | 0.0566 | - |
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+ | 1.2300 | 6450 | 0.096 | - |
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+ | 1.2395 | 6500 | 0.0883 | - |
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+ | 1.2490 | 6550 | 0.1103 | - |
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+ | 1.2586 | 6600 | 0.0496 | - |
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+ | 1.2681 | 6650 | 0.0596 | - |
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+ | 1.2777 | 6700 | 0.1329 | - |
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+ | 1.3063 | 6850 | 0.0848 | - |
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+ | 1.3158 | 6900 | 0.0751 | - |
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+ | 1.6400 | 8600 | 0.1586 | - |
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+ | 1.7353 | 9100 | 0.0916 | - |
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+ | 1.7544 | 9200 | 0.0694 | - |
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+ | 1.7639 | 9250 | 0.1176 | - |
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+ | 1.7735 | 9300 | 0.0666 | - |
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+ | 1.7830 | 9350 | 0.0738 | - |
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+ | 1.7925 | 9400 | 0.0818 | - |
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+ | 1.8116 | 9500 | 0.0816 | - |
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+ | 1.8211 | 9550 | 0.0682 | - |
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+ | 1.8307 | 9600 | 0.0743 | - |
335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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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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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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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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423
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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
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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
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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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559
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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
+ | 4.1667 | 21850 | 0.0523 | - |
580
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581
+ | 4.1857 | 21950 | 0.0514 | - |
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
+ | 4.4336 | 23250 | 0.1023 | - |
608
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609
+ | 4.4527 | 23350 | 0.046 | - |
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
+ | 4.5481 | 23850 | 0.0296 | - |
620
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621
+ | 4.5671 | 23950 | 0.0443 | - |
622
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623
+ | 4.5862 | 24050 | 0.0667 | - |
624
+ | 4.5957 | 24100 | 0.1331 | - |
625
+ | 4.6053 | 24150 | 0.0681 | - |
626
+ | 4.6148 | 24200 | 0.0526 | - |
627
+ | 4.6243 | 24250 | 0.0519 | - |
628
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629
+ | 4.6434 | 24350 | 0.1048 | - |
630
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631
+ | 4.6625 | 24450 | 0.0481 | - |
632
+ | 4.6720 | 24500 | 0.0957 | - |
633
+ | 4.6815 | 24550 | 0.0887 | - |
634
+ | 4.6911 | 24600 | 0.0607 | - |
635
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636
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637
+ | 4.7197 | 24750 | 0.0947 | - |
638
+ | 4.7292 | 24800 | 0.0482 | - |
639
+ | 4.7387 | 24850 | 0.0754 | - |
640
+ | 4.7483 | 24900 | 0.0865 | - |
641
+ | 4.7578 | 24950 | 0.1163 | - |
642
+ | 4.7674 | 25000 | 0.1251 | - |
643
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644
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645
+ | 4.7960 | 25150 | 0.0628 | - |
646
+ | 4.8055 | 25200 | 0.1018 | - |
647
+ | 4.8150 | 25250 | 0.0835 | - |
648
+ | 4.8246 | 25300 | 0.0812 | - |
649
+ | 4.8341 | 25350 | 0.074 | - |
650
+ | 4.8436 | 25400 | 0.0952 | - |
651
+ | 4.8532 | 25450 | 0.0409 | - |
652
+ | 4.8627 | 25500 | 0.0744 | - |
653
+ | 4.8722 | 25550 | 0.0849 | - |
654
+ | 4.8818 | 25600 | 0.0682 | - |
655
+ | 4.8913 | 25650 | 0.0953 | - |
656
+ | 4.9008 | 25700 | 0.0908 | - |
657
+ | 4.9104 | 25750 | 0.0972 | - |
658
+ | 4.9199 | 25800 | 0.0955 | - |
659
+ | 4.9294 | 25850 | 0.0845 | - |
660
+ | 4.9390 | 25900 | 0.1134 | - |
661
+ | 4.9485 | 25950 | 0.0545 | - |
662
+ | 4.9580 | 26000 | 0.0739 | - |
663
+ | 4.9676 | 26050 | 0.0926 | - |
664
+ | 4.9771 | 26100 | 0.0829 | - |
665
+ | 4.9867 | 26150 | 0.0796 | - |
666
+ | 4.9962 | 26200 | 0.0951 | - |
667
+ | 5.0057 | 26250 | 0.0885 | - |
668
+ | 5.0153 | 26300 | 0.1054 | - |
669
+ | 5.0248 | 26350 | 0.0546 | - |
670
+ | 5.0343 | 26400 | 0.0599 | - |
671
+ | 5.0439 | 26450 | 0.0803 | - |
672
+ | 5.0534 | 26500 | 0.1255 | - |
673
+ | 5.0629 | 26550 | 0.0419 | - |
674
+ | 5.0725 | 26600 | 0.0635 | - |
675
+ | 5.0820 | 26650 | 0.0734 | - |
676
+ | 5.0915 | 26700 | 0.0939 | - |
677
+ | 5.1011 | 26750 | 0.0613 | - |
678
+ | 5.1106 | 26800 | 0.0715 | - |
679
+ | 5.1201 | 26850 | 0.064 | - |
680
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681
+ | 5.1392 | 26950 | 0.0437 | - |
682
+ | 5.1487 | 27000 | 0.075 | - |
683
+ | 5.1583 | 27050 | 0.0759 | - |
684
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685
+ | 5.1773 | 27150 | 0.0847 | - |
686
+ | 5.1869 | 27200 | 0.07 | - |
687
+ | 5.1964 | 27250 | 0.082 | - |
688
+ | 5.2059 | 27300 | 0.0985 | - |
689
+ | 5.2155 | 27350 | 0.0758 | - |
690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
+ | 5.3585 | 28100 | 0.057 | - |
705
+ | 5.3680 | 28150 | 0.0514 | - |
706
+ | 5.3776 | 28200 | 0.0802 | - |
707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
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765
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766
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767
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
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811
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812
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813
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814
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815
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816
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817
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818
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819
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820
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821
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822
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823
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824
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825
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826
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827
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828
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829
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830
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831
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832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
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850
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851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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898
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899
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
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976
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977
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978
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979
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980
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981
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982
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983
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984
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985
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986
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987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
+ | 8.2094 | 43050 | 0.07 | - |
1004
+ | 8.2189 | 43100 | 0.0718 | - |
1005
+ | 8.2285 | 43150 | 0.0933 | - |
1006
+ | 8.2380 | 43200 | 0.0612 | - |
1007
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1008
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1009
+ | 8.2666 | 43350 | 0.0408 | - |
1010
+ | 8.2761 | 43400 | 0.0717 | - |
1011
+ | 8.2857 | 43450 | 0.0524 | - |
1012
+ | 8.2952 | 43500 | 0.1101 | - |
1013
+ | 8.3047 | 43550 | 0.1182 | - |
1014
+ | 8.3143 | 43600 | 0.0486 | - |
1015
+ | 8.3238 | 43650 | 0.09 | - |
1016
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1017
+ | 8.3429 | 43750 | 0.0569 | - |
1018
+ | 8.3524 | 43800 | 0.075 | - |
1019
+ | 8.3619 | 43850 | 0.0823 | - |
1020
+ | 8.3715 | 43900 | 0.0532 | - |
1021
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1022
+ | 8.3905 | 44000 | 0.0841 | - |
1023
+ | 8.4001 | 44050 | 0.09 | - |
1024
+ | 8.4096 | 44100 | 0.0974 | - |
1025
+ | 8.4191 | 44150 | 0.0373 | - |
1026
+ | 8.4287 | 44200 | 0.0778 | - |
1027
+ | 8.4382 | 44250 | 0.0499 | - |
1028
+ | 8.4477 | 44300 | 0.0537 | - |
1029
+ | 8.4573 | 44350 | 0.0604 | - |
1030
+ | 8.4668 | 44400 | 0.0509 | - |
1031
+ | 8.4764 | 44450 | 0.0763 | - |
1032
+ | 8.4859 | 44500 | 0.0995 | - |
1033
+ | 8.4954 | 44550 | 0.0737 | - |
1034
+ | 8.5050 | 44600 | 0.0567 | - |
1035
+ | 8.5145 | 44650 | 0.0701 | - |
1036
+ | 8.5240 | 44700 | 0.0717 | - |
1037
+ | 8.5336 | 44750 | 0.0899 | - |
1038
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1039
+ | 8.5526 | 44850 | 0.057 | - |
1040
+ | 8.5622 | 44900 | 0.0531 | - |
1041
+ | 8.5717 | 44950 | 0.0422 | - |
1042
+ | 8.5812 | 45000 | 0.0716 | - |
1043
+ | 8.5908 | 45050 | 0.0516 | - |
1044
+ | 8.6003 | 45100 | 0.0583 | - |
1045
+ | 8.6098 | 45150 | 0.0902 | - |
1046
+ | 8.6194 | 45200 | 0.0444 | - |
1047
+ | 8.6289 | 45250 | 0.0739 | - |
1048
+ | 8.6384 | 45300 | 0.0673 | - |
1049
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1050
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1051
+ | 8.6670 | 45450 | 0.0983 | - |
1052
+ | 8.6766 | 45500 | 0.0871 | - |
1053
+ | 8.6861 | 45550 | 0.0714 | - |
1054
+ | 8.6957 | 45600 | 0.0996 | - |
1055
+ | 8.7052 | 45650 | 0.0523 | - |
1056
+ | 8.7147 | 45700 | 0.0974 | - |
1057
+ | 8.7243 | 45750 | 0.084 | - |
1058
+ | 8.7338 | 45800 | 0.0973 | - |
1059
+ | 8.7433 | 45850 | 0.0553 | - |
1060
+ | 8.7529 | 45900 | 0.0729 | - |
1061
+ | 8.7624 | 45950 | 0.0338 | - |
1062
+ | 8.7719 | 46000 | 0.1203 | - |
1063
+ | 8.7815 | 46050 | 0.0721 | - |
1064
+ | 8.7910 | 46100 | 0.0371 | - |
1065
+ | 8.8005 | 46150 | 0.0707 | - |
1066
+ | 8.8101 | 46200 | 0.0682 | - |
1067
+ | 8.8196 | 46250 | 0.1348 | - |
1068
+ | 8.8291 | 46300 | 0.0753 | - |
1069
+ | 8.8387 | 46350 | 0.069 | - |
1070
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1071
+ | 8.8577 | 46450 | 0.0715 | - |
1072
+ | 8.8673 | 46500 | 0.0808 | - |
1073
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1080
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1102
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+
1192
+ ### Framework Versions
1193
+ - Python: 3.10.12
1194
+ - SetFit: 1.0.3
1195
+ - Sentence Transformers: 2.4.0
1196
+ - Transformers: 4.37.2
1197
+ - PyTorch: 2.1.0+cu121
1198
+ - Datasets: 2.17.1
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+ - Tokenizers: 0.15.2
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+
1201
+ ## Citation
1202
+
1203
+ ### BibTeX
1204
+ ```bibtex
1205
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1206
+ doi = {10.48550/ARXIV.2209.11055},
1207
+ url = {https://arxiv.org/abs/2209.11055},
1208
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1209
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1210
+ title = {Efficient Few-Shot Learning Without Prompts},
1211
+ publisher = {arXiv},
1212
+ year = {2022},
1213
+ copyright = {Creative Commons Attribution 4.0 International}
1214
+ }
1215
+ ```
1216
+
1217
+ <!--
1218
+ ## Glossary
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+
1220
+ *Clearly define terms in order to be accessible across audiences.*
1221
+ -->
1222
+
1223
+ <!--
1224
+ ## Model Card Authors
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+
1226
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1227
+ -->
1228
+
1229
+ <!--
1230
+ ## Model Card Contact
1231
+
1232
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
1233
+ -->
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