---
library_name: peft
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: beautiful-worm-91
  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. -->

# beautiful-worm-91

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: 0.5762
- Hamming Loss: 0.2815
- Zero One Loss: 1.0
- Jaccard Score: 0.8606
- Hamming Loss Optimised: 0.1121
- Hamming Loss Threshold: 0.7112
- Zero One Loss Optimised: 0.8762
- Zero One Loss Threshold: 0.5937
- Jaccard Score Optimised: 0.8487
- Jaccard Score Threshold: 0.3892

## 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: 1.27612271859294e-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: 7

### 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  | 0.7158          | 0.4196       | 1.0           | 0.8558        | 0.1123                 | 0.7884                 | 0.8688                  | 0.7125                  | 0.8208                  | 0.5703                  |
| No log        | 2.0   | 200  | 0.6802          | 0.3589       | 1.0           | 0.8562        | 0.1123                 | 0.8010                 | 0.89                    | 0.6870                  | 0.8545                  | 0.5943                  |
| No log        | 3.0   | 300  | 0.6461          | 0.3409       | 1.0           | 0.8701        | 0.1121                 | 0.7495                 | 0.885                   | 0.6670                  | 0.8457                  | 0.6441                  |
| No log        | 4.0   | 400  | 0.6162          | 0.3392       | 1.0           | 0.8767        | 0.1123                 | 0.7474                 | 0.8812                  | 0.6319                  | 0.8506                  | 0.4177                  |
| 0.6723        | 5.0   | 500  | 0.5936          | 0.3326       | 1.0           | 0.8785        | 0.1121                 | 0.7112                 | 0.8775                  | 0.6089                  | 0.8457                  | 0.6006                  |
| 0.6723        | 6.0   | 600  | 0.5804          | 0.2973       | 1.0           | 0.8651        | 0.1121                 | 0.7112                 | 0.875                   | 0.5979                  | 0.8447                  | 0.5836                  |
| 0.6723        | 7.0   | 700  | 0.5762          | 0.2815       | 1.0           | 0.8606        | 0.1121                 | 0.7112                 | 0.8762                  | 0.5937                  | 0.8487                  | 0.3892                  |


### Framework versions

- PEFT 0.13.2
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0