---
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