---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: t5_base_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_base_ledgar

This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5004
- Accuracy: 0.8664
- F1 Macro: 0.7948
- F1 Micro: 0.8664

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.443         | 0.11  | 100  | 1.1133          | 0.7291   | 0.5312   | 0.7291   |
| 0.8813        | 0.21  | 200  | 0.8404          | 0.7712   | 0.6296   | 0.7712   |
| 0.761         | 0.32  | 300  | 0.7386          | 0.8021   | 0.6789   | 0.8021   |
| 0.7358        | 0.43  | 400  | 0.7313          | 0.805    | 0.6787   | 0.805    |
| 0.7624        | 0.53  | 500  | 0.6561          | 0.8164   | 0.7134   | 0.8164   |
| 0.7067        | 0.64  | 600  | 0.6419          | 0.821    | 0.7273   | 0.821    |
| 0.6298        | 0.75  | 700  | 0.6412          | 0.8254   | 0.7230   | 0.8254   |
| 0.6544        | 0.85  | 800  | 0.6277          | 0.8217   | 0.7223   | 0.8217   |
| 0.5781        | 0.96  | 900  | 0.6054          | 0.8305   | 0.7420   | 0.8305   |
| 0.4674        | 1.07  | 1000 | 0.6210          | 0.8346   | 0.7371   | 0.8346   |
| 0.4929        | 1.17  | 1100 | 0.5876          | 0.8387   | 0.7423   | 0.8387   |
| 0.566         | 1.28  | 1200 | 0.5779          | 0.8475   | 0.7633   | 0.8475   |
| 0.4577        | 1.39  | 1300 | 0.5772          | 0.8435   | 0.7508   | 0.8435   |
| 0.4233        | 1.49  | 1400 | 0.5581          | 0.8476   | 0.7625   | 0.8476   |
| 0.4567        | 1.6   | 1500 | 0.5688          | 0.8462   | 0.7576   | 0.8462   |
| 0.483         | 1.71  | 1600 | 0.5547          | 0.8478   | 0.7609   | 0.8478   |
| 0.4649        | 1.81  | 1700 | 0.5396          | 0.851    | 0.7680   | 0.851    |
| 0.4288        | 1.92  | 1800 | 0.5235          | 0.8577   | 0.7759   | 0.8577   |
| 0.3445        | 2.03  | 1900 | 0.5204          | 0.8603   | 0.7791   | 0.8603   |
| 0.3014        | 2.13  | 2000 | 0.5269          | 0.8607   | 0.7862   | 0.8607   |
| 0.3301        | 2.24  | 2100 | 0.5234          | 0.8591   | 0.7826   | 0.8591   |
| 0.3069        | 2.35  | 2200 | 0.5266          | 0.8624   | 0.7851   | 0.8624   |
| 0.3095        | 2.45  | 2300 | 0.5155          | 0.8629   | 0.7846   | 0.8629   |
| 0.3164        | 2.56  | 2400 | 0.5106          | 0.8646   | 0.7909   | 0.8646   |
| 0.2914        | 2.67  | 2500 | 0.5055          | 0.8647   | 0.7934   | 0.8647   |
| 0.2946        | 2.77  | 2600 | 0.5027          | 0.8643   | 0.7917   | 0.8643   |
| 0.3012        | 2.88  | 2700 | 0.5009          | 0.8671   | 0.7953   | 0.8671   |
| 0.3181        | 2.99  | 2800 | 0.5004          | 0.8664   | 0.7948   | 0.8664   |


### Framework versions

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