---
library_name: transformers
base_model: sandernotenbaert/okai-musiclang-content-t5-small_finetune
tags:
- generated_from_trainer
model-index:
- name: okai-musiclang-content-t5-small_finetune
  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. -->

# okai-musiclang-content-t5-small_finetune

This model is a fine-tuned version of [sandernotenbaert/okai-musiclang-content-t5-small_finetune](https://huggingface.co/sandernotenbaert/okai-musiclang-content-t5-small_finetune) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5041

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.7839        | 0.2226 | 500   | 1.6513          |
| 1.6576        | 0.4452 | 1000  | 1.6615          |
| 1.6396        | 0.6679 | 1500  | 1.6650          |
| 1.7168        | 0.8905 | 2000  | 1.6315          |
| 1.7366        | 1.1131 | 2500  | 1.6234          |
| 1.7171        | 1.3357 | 3000  | 1.6028          |
| 1.6238        | 1.5583 | 3500  | 1.6130          |
| 1.6217        | 1.7810 | 4000  | 1.6218          |
| 1.7077        | 2.0036 | 4500  | 1.5784          |
| 1.7034        | 2.2262 | 5000  | 1.5792          |
| 1.6049        | 2.4488 | 5500  | 1.5866          |
| 1.6018        | 2.6714 | 6000  | 1.5869          |
| 1.6628        | 2.8941 | 6500  | 1.5628          |
| 1.653         | 3.1171 | 7000  | 1.5606          |
| 1.6575        | 3.3397 | 7500  | 1.5381          |
| 1.64          | 3.5619 | 8000  | 1.5395          |
| 1.6455        | 3.7845 | 8500  | 1.5163          |
| 1.6308        | 4.0076 | 9000  | 1.5311          |
| 1.6324        | 4.2302 | 9500  | 1.5118          |
| 1.5481        | 4.4528 | 10000 | 1.5092          |
| 1.547         | 4.6754 | 10500 | 1.5109          |
| 1.5584        | 4.8981 | 11000 | 1.5041          |


### Framework versions

- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2