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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: trusting-cod-535
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trusting-cod-535
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.
It achieves the following results on the evaluation set:
- Loss: 0.1752
- Hamming Loss: 0.0605
- Zero One Loss: 0.37
- Jaccard Score: 0.3239
- Hamming Loss Optimised: 0.0591
- Hamming Loss Threshold: 0.5959
- Zero One Loss Optimised: 0.3675
- Zero One Loss Threshold: 0.4856
- Jaccard Score Optimised: 0.3093
- Jaccard Score Threshold: 0.3560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4.347554938953255e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-------------:|:-------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
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