| ---
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| library_name: transformers
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| license: apache-2.0
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| base_model: google-bert/bert-base-cased
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| tags:
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| - generated_from_trainer
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| model-index:
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| - name: trusting-cod-535
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| results: []
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| ---
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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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|
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| # trusting-cod-535
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| This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
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| It achieves the following results on the evaluation set:
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| - Loss: 0.1752
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| - Hamming Loss: 0.0605
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| - Zero One Loss: 0.37
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| - Jaccard Score: 0.3239
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| - Hamming Loss Optimised: 0.0591
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| - Hamming Loss Threshold: 0.5959
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| - Zero One Loss Optimised: 0.3675
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| - Zero One Loss Threshold: 0.4856
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| - Jaccard Score Optimised: 0.3093
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| - Jaccard Score Threshold: 0.3560
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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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|
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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: 4.347554938953255e-05
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| - train_batch_size: 32
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| - eval_batch_size: 32
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| - seed: 2024
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| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| - lr_scheduler_type: linear
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| - num_epochs: 8
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| ### Training results
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| | Training Loss | Epoch | Step | Validation Loss | Hamming Loss | Zero One Loss | Jaccard Score | Hamming Loss Optimised | Hamming Loss Threshold | Zero One Loss Optimised | Zero One Loss Threshold | Jaccard Score Optimised | Jaccard Score Threshold |
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| |:-------------:|:-----:|:----:|:---------------:|:------------:|:-------------:|:-------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|
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| | No log | 1.0 | 100 | 0.2265 | 0.0686 | 0.5975 | 0.5674 | 0.0679 | 0.4120 | 0.51 | 0.2533 | 0.4322 | 0.2167 |
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| | No log | 2.0 | 200 | 0.1854 | 0.0619 | 0.49 | 0.4391 | 0.0591 | 0.5284 | 0.4788 | 0.4056 | 0.3473 | 0.2884 |
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| | No log | 3.0 | 300 | 0.1695 | 0.0594 | 0.4425 | 0.3997 | 0.0592 | 0.5274 | 0.4025 | 0.4051 | 0.3220 | 0.3099 |
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| | No log | 4.0 | 400 | 0.1668 | 0.0569 | 0.4012 | 0.3566 | 0.0565 | 0.5047 | 0.3862 | 0.4056 | 0.3123 | 0.3389 |
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| | 0.1794 | 5.0 | 500 | 0.1698 | 0.0591 | 0.38 | 0.3274 | 0.0579 | 0.5888 | 0.38 | 0.4808 | 0.3050 | 0.2804 |
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| | 0.1794 | 6.0 | 600 | 0.1718 | 0.0615 | 0.38 | 0.3278 | 0.0596 | 0.6098 | 0.375 | 0.4379 | 0.3058 | 0.3497 |
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| | 0.1794 | 7.0 | 700 | 0.1739 | 0.0611 | 0.3762 | 0.3301 | 0.0594 | 0.5669 | 0.37 | 0.4056 | 0.3071 | 0.3564 |
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| | 0.1794 | 8.0 | 800 | 0.1752 | 0.0605 | 0.37 | 0.3239 | 0.0591 | 0.5959 | 0.3675 | 0.4856 | 0.3093 | 0.3560 |
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| ### Framework versions
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| - Transformers 4.47.0
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| - Pytorch 2.5.1+cu124
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| - Datasets 3.1.0
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| - Tokenizers 0.21.0
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