---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: t5_small_ledgar
  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. -->

# t5_small_ledgar

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5465
- Accuracy: 0.8527
- F1 Macro: 0.7698
- F1 Micro: 0.8527

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 2.3898        | 0.11  | 100  | 1.8531          | 0.6083   | 0.3305   | 0.6083   |
| 1.1887        | 0.21  | 200  | 1.0730          | 0.7307   | 0.5340   | 0.7307   |
| 0.946         | 0.32  | 300  | 0.8826          | 0.77     | 0.6068   | 0.77     |
| 0.8383        | 0.43  | 400  | 0.8016          | 0.7851   | 0.6351   | 0.7851   |
| 0.8559        | 0.53  | 500  | 0.7437          | 0.8011   | 0.6747   | 0.8011   |
| 0.7944        | 0.64  | 600  | 0.7068          | 0.8091   | 0.6933   | 0.8091   |
| 0.7151        | 0.75  | 700  | 0.6853          | 0.8191   | 0.6983   | 0.8191   |
| 0.7077        | 0.85  | 800  | 0.6666          | 0.8187   | 0.7120   | 0.8187   |
| 0.6645        | 0.96  | 900  | 0.6476          | 0.8196   | 0.7211   | 0.8196   |
| 0.5918        | 1.07  | 1000 | 0.6469          | 0.8297   | 0.7262   | 0.8297   |
| 0.5866        | 1.17  | 1100 | 0.6309          | 0.8288   | 0.7286   | 0.8288   |
| 0.6665        | 1.28  | 1200 | 0.6188          | 0.8363   | 0.7473   | 0.8363   |
| 0.5684        | 1.39  | 1300 | 0.6118          | 0.837    | 0.7456   | 0.837    |
| 0.4986        | 1.49  | 1400 | 0.6117          | 0.8374   | 0.7520   | 0.8374   |
| 0.5786        | 1.6   | 1500 | 0.6104          | 0.8363   | 0.7462   | 0.8363   |
| 0.5956        | 1.71  | 1600 | 0.5965          | 0.8365   | 0.7455   | 0.8365   |
| 0.5653        | 1.81  | 1700 | 0.5817          | 0.8425   | 0.7588   | 0.8425   |
| 0.5292        | 1.92  | 1800 | 0.5732          | 0.842    | 0.7516   | 0.842    |
| 0.4674        | 2.03  | 1900 | 0.5670          | 0.8456   | 0.7544   | 0.8456   |
| 0.452         | 2.13  | 2000 | 0.5686          | 0.847    | 0.7615   | 0.847    |
| 0.4827        | 2.24  | 2100 | 0.5636          | 0.8461   | 0.7716   | 0.8461   |
| 0.4617        | 2.35  | 2200 | 0.5611          | 0.8491   | 0.7613   | 0.8491   |
| 0.4508        | 2.45  | 2300 | 0.5594          | 0.8499   | 0.7610   | 0.8499   |
| 0.432         | 2.56  | 2400 | 0.5532          | 0.85     | 0.7654   | 0.85     |
| 0.4298        | 2.67  | 2500 | 0.5521          | 0.8503   | 0.7666   | 0.8503   |
| 0.4627        | 2.77  | 2600 | 0.5511          | 0.85     | 0.7661   | 0.85     |
| 0.4353        | 2.88  | 2700 | 0.5466          | 0.8532   | 0.7706   | 0.8532   |
| 0.4371        | 2.99  | 2800 | 0.5465          | 0.8527   | 0.7698   | 0.8527   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2