---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: t5_small_amazon
  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_amazon

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: 0.7014
- Accuracy: 0.7767
- F1 Macro: 0.7273
- F1 Micro: 0.7767

## 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: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 2.1202        | 0.13  | 50   | 1.8088          | 0.5329   | 0.3841   | 0.5329   |
| 1.1993        | 0.26  | 100  | 1.1746          | 0.6614   | 0.5439   | 0.6614   |
| 1.0698        | 0.39  | 150  | 1.0116          | 0.6884   | 0.6004   | 0.6884   |
| 0.8999        | 0.53  | 200  | 0.9428          | 0.7174   | 0.6539   | 0.7174   |
| 1.1022        | 0.66  | 250  | 0.8932          | 0.7246   | 0.6716   | 0.7246   |
| 0.8337        | 0.79  | 300  | 0.8664          | 0.7286   | 0.6789   | 0.7286   |
| 0.9594        | 0.92  | 350  | 0.8445          | 0.7503   | 0.6994   | 0.7503   |
| 0.803         | 1.05  | 400  | 0.8048          | 0.7530   | 0.6996   | 0.7530   |
| 0.7271        | 1.18  | 450  | 0.7776          | 0.7602   | 0.7019   | 0.7602   |
| 0.6694        | 1.32  | 500  | 0.7674          | 0.7609   | 0.7084   | 0.7609   |
| 0.6109        | 1.45  | 550  | 0.7648          | 0.7609   | 0.7081   | 0.7609   |
| 0.6575        | 1.58  | 600  | 0.7527          | 0.7628   | 0.7117   | 0.7628   |
| 0.777         | 1.71  | 650  | 0.7419          | 0.7694   | 0.7218   | 0.7694   |
| 0.6362        | 1.84  | 700  | 0.7272          | 0.7800   | 0.7301   | 0.7800   |
| 0.648         | 1.97  | 750  | 0.7137          | 0.7813   | 0.7356   | 0.7813   |
| 0.4981        | 2.11  | 800  | 0.7154          | 0.7767   | 0.7258   | 0.7767   |
| 0.4955        | 2.24  | 850  | 0.7233          | 0.7800   | 0.7318   | 0.7800   |
| 0.4451        | 2.37  | 900  | 0.7182          | 0.7780   | 0.7280   | 0.7780   |
| 0.421         | 2.5   | 950  | 0.7117          | 0.7747   | 0.7262   | 0.7747   |
| 0.4853        | 2.63  | 1000 | 0.7092          | 0.7760   | 0.7272   | 0.7760   |
| 0.5442        | 2.76  | 1050 | 0.7114          | 0.7740   | 0.7272   | 0.7740   |
| 0.4863        | 2.89  | 1100 | 0.7014          | 0.7767   | 0.7273   | 0.7767   |


### Framework versions

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