---
library_name: peft
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: grandiose-horse-172
  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. -->

# grandiose-horse-172

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: 0.6509
- Hamming Loss: 0.3414
- Zero One Loss: 1.0
- Jaccard Score: 0.8678
- Hamming Loss Optimised: 0.1121
- Hamming Loss Threshold: 0.7504
- Zero One Loss Optimised: 0.8812
- Zero One Loss Threshold: 0.6730
- Jaccard Score Optimised: 0.8449
- Jaccard Score Threshold: 0.6539

## 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: 1.510606094120106e-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: 5

### 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  | 0.7202          | 0.4325       | 1.0           | 0.8586        | 0.1123                 | 0.7924                 | 0.8712                  | 0.7112                  | 0.8203                  | 0.5766                  |
| No log        | 2.0   | 200  | 0.6922          | 0.3761       | 1.0           | 0.8520        | 0.1123                 | 0.7829                 | 0.8812                  | 0.6982                  | 0.8546                  | 0.5904                  |
| No log        | 3.0   | 300  | 0.6696          | 0.349        | 1.0           | 0.8606        | 0.1123                 | 0.7641                 | 0.885                   | 0.6857                  | 0.8436                  | 0.6634                  |
| No log        | 4.0   | 400  | 0.6555          | 0.3432       | 1.0           | 0.8662        | 0.1121                 | 0.7518                 | 0.8825                  | 0.6757                  | 0.8455                  | 0.6604                  |
| 0.6931        | 5.0   | 500  | 0.6509          | 0.3414       | 1.0           | 0.8678        | 0.1121                 | 0.7504                 | 0.8812                  | 0.6730                  | 0.8449                  | 0.6539                  |


### Framework versions

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