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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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- name: rule_learning_margin_1mm
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# rule_learning_margin_1mm
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3813
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- Margin Accuracy: 0.8240
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 2000
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- total_train_batch_size: 8000
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|
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| 0.6482 | 0.16 | 20 | 0.6494 | 0.7263 |
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| 0.5151 | 0.32 | 40 | 0.5088 | 0.7792 |
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| 0.4822 | 0.48 | 60 | 0.4429 | 0.8045 |
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| 0.4472 | 0.64 | 80 | 0.4265 | 0.8107 |
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| 0.4352 | 0.8 | 100 | 0.4155 | 0.8132 |
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| 0.4335 | 0.96 | 120 | 0.4128 | 0.8116 |
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| 0.4113 | 1.12 | 140 | 0.4119 | 0.8142 |
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| 0.4186 | 1.28 | 160 | 0.4075 | 0.8120 |
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| 0.42 | 1.44 | 180 | 0.4072 | 0.8123 |
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| 0.4175 | 1.6 | 200 | 0.4080 | 0.8130 |
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| 0.4097 | 1.76 | 220 | 0.4031 | 0.8128 |
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| 0.397 | 1.92 | 240 | 0.4004 | 0.8130 |
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| 0.4115 | 2.08 | 260 | 0.3979 | 0.8136 |
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| 0.4108 | 2.24 | 280 | 0.3940 | 0.8167 |
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| 0.4125 | 2.4 | 300 | 0.3879 | 0.8218 |
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| 0.4117 | 2.56 | 320 | 0.3848 | 0.8217 |
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| 0.3967 | 2.72 | 340 | 0.3818 | 0.8231 |
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| 0.3947 | 2.88 | 360 | 0.3813 | 0.8240 |
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### Framework versions
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- Transformers 4.19.2
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- Pytorch 1.11.0
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- Datasets 2.2.1
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- Tokenizers 0.12.1
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