---
library_name: peft
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: masked-slug-729
  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. -->

# masked-slug-729

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5084
- Hamming Loss: 0.1123
- Zero One Loss: 0.995
- Jaccard Score: 0.995
- Hamming Loss Optimised: 0.1123
- Hamming Loss Threshold: 0.5944
- Zero One Loss Optimised: 1.0
- Zero One Loss Threshold: 0.9000
- Jaccard Score Optimised: 0.7627
- Jaccard Score Threshold: 0.4303

## 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: 5.408225206539412e-06
- train_batch_size: 4
- eval_batch_size: 4
- 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: 6

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-------------:|:-------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|
| 0.6838        | 1.0   | 800  | 0.6757          | 0.4291       | 1.0           | 0.8717        | 0.1123                 | 0.5944                 | 1.0                     | 0.9000                  | 0.8878                  | 0.2889                  |
| 0.6577        | 2.0   | 1600 | 0.6238          | 0.2076       | 0.915         | 0.8579        | 0.1123                 | 0.5944                 | 1.0                     | 0.9000                  | 0.8224                  | 0.4788                  |
| 0.6103        | 3.0   | 2400 | 0.5443          | 0.113        | 0.9888        | 0.9888        | 0.1123                 | 0.5944                 | 1.0                     | 0.9000                  | 0.8487                  | 0.4056                  |
| 0.5495        | 4.0   | 3200 | 0.5206          | 0.1125       | 0.9938        | 0.9938        | 0.1123                 | 0.5944                 | 1.0                     | 0.9000                  | 0.7640                  | 0.4489                  |
| 0.5332        | 5.0   | 4000 | 0.5112          | 0.1123       | 0.995         | 0.995         | 0.1123                 | 0.5944                 | 1.0                     | 0.9000                  | 0.7633                  | 0.4459                  |
| 0.5304        | 6.0   | 4800 | 0.5084          | 0.1123       | 0.995         | 0.995         | 0.1123                 | 0.5944                 | 1.0                     | 0.9000                  | 0.7627                  | 0.4303                  |


### Framework versions

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