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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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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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metrics: |
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- accuracy |
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widget: |
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- text: BI 8U-Q10-AP6X2-V1131 SENSOR QUICK DISCO |
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- text: 48-08-0551 FOLDING MITRE SAW STAND |
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- text: JAS-LEB04-M3 COMPACT SPEED CONTROLLER |
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- text: LWFS37C2R1025HS2/E37.5 RAIL |
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- text: '300108' |
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pipeline_tag: text-classification |
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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: 0.3217244143582435 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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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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The model has been trained using an efficient few-shot learning technique that involves: |
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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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## Model Details |
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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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### Model Sources |
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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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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.3217 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("amitprgx/setfit-categorization") |
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# Run inference |
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preds = model("300108") |
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``` |
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<!-- |
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### Downstream Use |
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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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### Out-of-Scope Use |
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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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## Bias, Risks and Limitations |
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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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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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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 | 4.7197 | 10 | |
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### Training Hyperparameters |
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- batch_size: (8, 8) |
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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: 20 |
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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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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:-----:|:-------------:|:---------------:| |
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| 0.0008 | 1 | 0.1444 | - | |
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| 0.0379 | 50 | 0.1563 | - | |
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| 0.0758 | 100 | 0.2163 | - | |
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| 0.1136 | 150 | 0.3125 | - | |
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| 0.1515 | 200 | 0.2152 | - | |
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| 0.1894 | 250 | 0.2731 | - | |
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| 0.2273 | 300 | 0.2788 | - | |
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| 0.2652 | 350 | 0.2315 | - | |
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| 0.3030 | 400 | 0.1847 | - | |
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| 0.3409 | 450 | 0.1253 | - | |
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| 0.3788 | 500 | 0.1363 | - | |
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| 0.4167 | 550 | 0.1816 | - | |
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| 0.4545 | 600 | 0.1957 | - | |
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| 0.4924 | 650 | 0.1931 | - | |
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| 0.5303 | 700 | 0.1392 | - | |
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| 0.5682 | 750 | 0.0613 | - | |
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| 0.6061 | 800 | 0.0403 | - | |
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| 0.6439 | 850 | 0.0796 | - | |
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| 0.6818 | 900 | 0.0661 | - | |
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| 0.7197 | 950 | 0.1207 | - | |
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| 0.7576 | 1000 | 0.0795 | - | |
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| 0.7955 | 1050 | 0.0439 | - | |
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| 0.8333 | 1100 | 0.0744 | - | |
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| 0.8712 | 1150 | 0.0972 | - | |
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| 0.9091 | 1200 | 0.0512 | - | |
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| 0.9470 | 1250 | 0.0335 | - | |
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| 0.9848 | 1300 | 0.0092 | - | |
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| 1.0227 | 1350 | 0.0489 | - | |
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| 1.0606 | 1400 | 0.0176 | - | |
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| 1.0985 | 1450 | 0.0302 | - | |
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| 1.1364 | 1500 | 0.0811 | - | |
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| 1.1742 | 1550 | 0.0181 | - | |
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| 1.2121 | 1600 | 0.0354 | - | |
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| 1.25 | 1650 | 0.0183 | - | |
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| 1.2879 | 1700 | 0.0167 | - | |
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| 1.3258 | 1750 | 0.006 | - | |
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| 1.3636 | 1800 | 0.0294 | - | |
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| 1.4015 | 1850 | 0.0342 | - | |
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| 1.4394 | 1900 | 0.005 | - | |
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| 1.4773 | 1950 | 0.0044 | - | |
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| 1.5152 | 2000 | 0.0069 | - | |
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| 1.5530 | 2050 | 0.0051 | - | |
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| 1.5909 | 2100 | 0.0375 | - | |
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| 1.6288 | 2150 | 0.0123 | - | |
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| 1.6667 | 2200 | 0.0058 | - | |
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| 1.7045 | 2250 | 0.0086 | - | |
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| 1.7424 | 2300 | 0.0141 | - | |
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| 1.7803 | 2350 | 0.0014 | - | |
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| 1.8182 | 2400 | 0.0047 | - | |
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| 1.8561 | 2450 | 0.0018 | - | |
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| 1.8939 | 2500 | 0.0063 | - | |
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| 1.9318 | 2550 | 0.0018 | - | |
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| 1.9697 | 2600 | 0.0032 | - | |
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| 2.0076 | 2650 | 0.001 | - | |
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| 2.0455 | 2700 | 0.0165 | - | |
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| 2.0833 | 2750 | 0.0773 | - | |
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| 2.1212 | 2800 | 0.0014 | - | |
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| 2.1591 | 2850 | 0.0105 | - | |
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| 2.1970 | 2900 | 0.0013 | - | |
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| 2.2348 | 2950 | 0.0009 | - | |
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| 2.2727 | 3000 | 0.0034 | - | |
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| 2.3106 | 3050 | 0.0013 | - | |
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| 2.3485 | 3100 | 0.0065 | - | |
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| 2.3864 | 3150 | 0.0008 | - | |
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| 2.4242 | 3200 | 0.1143 | - | |
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| 2.4621 | 3250 | 0.0036 | - | |
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| 2.5 | 3300 | 0.0254 | - | |
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| 2.5379 | 3350 | 0.0023 | - | |
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| 2.5758 | 3400 | 0.004 | - | |
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| 2.6136 | 3450 | 0.0034 | - | |
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| 2.6515 | 3500 | 0.0019 | - | |
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| 2.6894 | 3550 | 0.001 | - | |
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| 2.7273 | 3600 | 0.1044 | - | |
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| 2.7652 | 3650 | 0.0005 | - | |
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| 2.8030 | 3700 | 0.0955 | - | |
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| 2.8409 | 3750 | 0.0011 | - | |
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| 2.8788 | 3800 | 0.0018 | - | |
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| 2.9167 | 3850 | 0.0017 | - | |
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| 2.9545 | 3900 | 0.0007 | - | |
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| 2.9924 | 3950 | 0.001 | - | |
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| 3.0303 | 4000 | 0.0009 | - | |
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| 3.0682 | 4050 | 0.001 | - | |
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| 3.1061 | 4100 | 0.0035 | - | |
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| 3.1439 | 4150 | 0.0009 | - | |
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| 3.1818 | 4200 | 0.0009 | - | |
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| 3.2197 | 4250 | 0.0005 | - | |
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| 3.2576 | 4300 | 0.0011 | - | |
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| 3.2955 | 4350 | 0.0007 | - | |
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| 3.3333 | 4400 | 0.0007 | - | |
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| 3.3712 | 4450 | 0.0003 | - | |
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| 3.4091 | 4500 | 0.0008 | - | |
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| 3.4470 | 4550 | 0.0007 | - | |
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| 3.4848 | 4600 | 0.0004 | - | |
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| 3.5227 | 4650 | 0.0011 | - | |
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| 3.5606 | 4700 | 0.0009 | - | |
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| 3.5985 | 4750 | 0.0004 | - | |
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| 3.6364 | 4800 | 0.0006 | - | |
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| 3.6742 | 4850 | 0.0012 | - | |
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| 3.7121 | 4900 | 0.0004 | - | |
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| 3.75 | 4950 | 0.0003 | - | |
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| 3.7879 | 5000 | 0.0005 | - | |
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| 3.8258 | 5050 | 0.0007 | - | |
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| 3.8636 | 5100 | 0.0012 | - | |
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| 3.9015 | 5150 | 0.0003 | - | |
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| 3.9394 | 5200 | 0.0009 | - | |
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| 3.9773 | 5250 | 0.0003 | - | |
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| 4.0152 | 5300 | 0.0003 | - | |
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| 4.0530 | 5350 | 0.0005 | - | |
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| 4.0909 | 5400 | 0.0004 | - | |
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| 4.1288 | 5450 | 0.0003 | - | |
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| 4.1667 | 5500 | 0.0003 | - | |
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| 4.2045 | 5550 | 0.0011 | - | |
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| 4.2424 | 5600 | 0.0002 | - | |
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| 4.2803 | 5650 | 0.0004 | - | |
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| 4.3182 | 5700 | 0.0009 | - | |
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| 4.3561 | 5750 | 0.0003 | - | |
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| 4.3939 | 5800 | 0.0002 | - | |
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| 4.4318 | 5850 | 0.0008 | - | |
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| 4.4697 | 5900 | 0.0003 | - | |
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| 4.5076 | 5950 | 0.0004 | - | |
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| 4.5455 | 6000 | 0.0272 | - | |
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| 4.5833 | 6050 | 0.0012 | - | |
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| 4.6212 | 6100 | 0.0006 | - | |
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| 4.6591 | 6150 | 0.0005 | - | |
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| 4.6970 | 6200 | 0.0011 | - | |
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| 4.7348 | 6250 | 0.0003 | - | |
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| 4.7727 | 6300 | 0.0003 | - | |
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| 4.8106 | 6350 | 0.0026 | - | |
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| 4.8485 | 6400 | 0.0007 | - | |
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| 4.8864 | 6450 | 0.0002 | - | |
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| 4.9242 | 6500 | 0.0007 | - | |
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| 4.9621 | 6550 | 0.0004 | - | |
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| 5.0 | 6600 | 0.0002 | - | |
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| 5.0379 | 6650 | 0.0002 | - | |
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| 5.0758 | 6700 | 0.0003 | - | |
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| 5.1136 | 6750 | 0.0004 | - | |
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| 5.1515 | 6800 | 0.0007 | - | |
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| 5.1894 | 6850 | 0.0002 | - | |
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| 5.2273 | 6900 | 0.0002 | - | |
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| 5.2652 | 6950 | 0.0001 | - | |
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| 5.3030 | 7000 | 0.0003 | - | |
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| 5.3409 | 7050 | 0.0001 | - | |
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| 5.3788 | 7100 | 0.0002 | - | |
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| 5.4167 | 7150 | 0.0003 | - | |
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| 5.4545 | 7200 | 0.0006 | - | |
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| 5.4924 | 7250 | 0.0002 | - | |
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| 5.5303 | 7300 | 0.0002 | - | |
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| 5.5682 | 7350 | 0.0002 | - | |
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| 5.6061 | 7400 | 0.0004 | - | |
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| 5.6439 | 7450 | 0.0003 | - | |
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| 5.6818 | 7500 | 0.0002 | - | |
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| 5.7197 | 7550 | 0.0002 | - | |
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| 5.7576 | 7600 | 0.0002 | - | |
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| 5.7955 | 7650 | 0.0005 | - | |
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| 5.8333 | 7700 | 0.0013 | - | |
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| 5.8712 | 7750 | 0.0002 | - | |
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| 5.9091 | 7800 | 0.0015 | - | |
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| 5.9470 | 7850 | 0.0001 | - | |
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| 5.9848 | 7900 | 0.0002 | - | |
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| 6.0227 | 7950 | 0.0001 | - | |
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| 6.0606 | 8000 | 0.0015 | - | |
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| 6.0985 | 8050 | 0.0004 | - | |
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| 6.1364 | 8100 | 0.0373 | - | |
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| 6.1742 | 8150 | 0.0003 | - | |
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| 6.2121 | 8200 | 0.0002 | - | |
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| 6.25 | 8250 | 0.0003 | - | |
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| 6.2879 | 8300 | 0.0003 | - | |
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| 6.3258 | 8350 | 0.0003 | - | |
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| 6.3636 | 8400 | 0.0002 | - | |
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| 6.4015 | 8450 | 0.0001 | - | |
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| 6.4394 | 8500 | 0.0004 | - | |
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| 6.4773 | 8550 | 0.0002 | - | |
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| 6.5152 | 8600 | 0.0002 | - | |
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| 6.5530 | 8650 | 0.0002 | - | |
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| 6.5909 | 8700 | 0.0004 | - | |
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| 6.6288 | 8750 | 0.0002 | - | |
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| 6.6667 | 8800 | 0.0001 | - | |
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| 6.7045 | 8850 | 0.0003 | - | |
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| 6.7424 | 8900 | 0.0001 | - | |
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| 6.7803 | 8950 | 0.0002 | - | |
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| 6.8182 | 9000 | 0.0003 | - | |
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| 6.8561 | 9050 | 0.0002 | - | |
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| 6.8939 | 9100 | 0.0002 | - | |
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| 6.9318 | 9150 | 0.0001 | - | |
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| 6.9697 | 9200 | 0.0001 | - | |
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| 7.0076 | 9250 | 0.0002 | - | |
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| 7.0455 | 9300 | 0.0002 | - | |
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| 7.0833 | 9350 | 0.0002 | - | |
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| 7.1212 | 9400 | 0.0001 | - | |
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| 7.1591 | 9450 | 0.0002 | - | |
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| 7.1970 | 9500 | 0.0003 | - | |
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| 7.2348 | 9550 | 0.0005 | - | |
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| 7.2727 | 9600 | 0.0002 | - | |
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| 7.3106 | 9650 | 0.0002 | - | |
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| 7.3485 | 9700 | 0.0002 | - | |
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| 7.3864 | 9750 | 0.0002 | - | |
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| 7.4242 | 9800 | 0.0002 | - | |
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| 7.4621 | 9850 | 0.0001 | - | |
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| 7.5 | 9900 | 0.0001 | - | |
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| 7.5379 | 9950 | 0.0002 | - | |
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| 7.5758 | 10000 | 0.0001 | - | |
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| 7.6136 | 10050 | 0.0001 | - | |
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| 7.6515 | 10100 | 0.0001 | - | |
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| 7.6894 | 10150 | 0.0002 | - | |
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| 7.7273 | 10200 | 0.0002 | - | |
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| 7.7652 | 10250 | 0.0001 | - | |
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| 7.8030 | 10300 | 0.0002 | - | |
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| 7.8409 | 10350 | 0.0003 | - | |
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| 7.8788 | 10400 | 0.0002 | - | |
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| 7.9167 | 10450 | 0.0002 | - | |
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| 7.9545 | 10500 | 0.0001 | - | |
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| 7.9924 | 10550 | 0.0002 | - | |
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| 8.0303 | 10600 | 0.0002 | - | |
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| 8.0682 | 10650 | 0.0002 | - | |
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| 8.1061 | 10700 | 0.0002 | - | |
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| 8.1439 | 10750 | 0.0001 | - | |
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| 8.1818 | 10800 | 0.0001 | - | |
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| 8.2197 | 10850 | 0.0001 | - | |
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| 8.2576 | 10900 | 0.0001 | - | |
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| 8.2955 | 10950 | 0.0001 | - | |
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| 8.3333 | 11000 | 0.0002 | - | |
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| 8.3712 | 11050 | 0.0007 | - | |
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| 8.4091 | 11100 | 0.0001 | - | |
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| 8.4470 | 11150 | 0.0002 | - | |
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| 8.4848 | 11200 | 0.0001 | - | |
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| 8.5227 | 11250 | 0.0002 | - | |
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| 8.5606 | 11300 | 0.0001 | - | |
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| 8.5985 | 11350 | 0.0001 | - | |
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| 8.6364 | 11400 | 0.0001 | - | |
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| 8.6742 | 11450 | 0.0001 | - | |
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| 8.7121 | 11500 | 0.0002 | - | |
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| 8.75 | 11550 | 0.0001 | - | |
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| 8.7879 | 11600 | 0.0001 | - | |
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| 8.8258 | 11650 | 0.0001 | - | |
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| 8.8636 | 11700 | 0.0001 | - | |
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| 8.9015 | 11750 | 0.0001 | - | |
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| 8.9394 | 11800 | 0.0001 | - | |
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| 8.9773 | 11850 | 0.0001 | - | |
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| 9.0152 | 11900 | 0.0001 | - | |
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| 9.0530 | 11950 | 0.0001 | - | |
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| 9.0909 | 12000 | 0.0001 | - | |
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| 9.1288 | 12050 | 0.0001 | - | |
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| 9.1667 | 12100 | 0.0002 | - | |
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| 9.2045 | 12150 | 0.0001 | - | |
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| 9.2424 | 12200 | 0.0001 | - | |
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| 9.2803 | 12250 | 0.0002 | - | |
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| 9.3182 | 12300 | 0.0002 | - | |
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| 9.3561 | 12350 | 0.0002 | - | |
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| 9.3939 | 12400 | 0.0001 | - | |
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| 9.4318 | 12450 | 0.0003 | - | |
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| 9.4697 | 12500 | 0.0001 | - | |
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| 9.5076 | 12550 | 0.0001 | - | |
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| 9.5455 | 12600 | 0.0001 | - | |
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| 9.5833 | 12650 | 0.0002 | - | |
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| 9.6212 | 12700 | 0.0001 | - | |
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| 9.6591 | 12750 | 0.0002 | - | |
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| 9.6970 | 12800 | 0.0002 | - | |
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| 9.7348 | 12850 | 0.0001 | - | |
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| 9.7727 | 12900 | 0.0001 | - | |
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| 9.8106 | 12950 | 0.0001 | - | |
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| 9.8485 | 13000 | 0.0001 | - | |
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| 9.8864 | 13050 | 0.0001 | - | |
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| 9.9242 | 13100 | 0.0001 | - | |
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| 9.9621 | 13150 | 0.0001 | - | |
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| 10.0 | 13200 | 0.0002 | - | |
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### Framework Versions |
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- Python: 3.11.8 |
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- SetFit: 1.1.0.dev0 |
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- Sentence Transformers: 2.6.1 |
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- Transformers: 4.39.3 |
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- PyTorch: 1.13.1+cu117 |
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- Datasets: 2.19.0 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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