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

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: 0.5565
- Accuracy: 0.8399
- F1 Macro: 0.8113
- F1 Micro: 0.8399

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.2275        | 0.13  | 50   | 1.0353          | 0.6950   | 0.6073   | 0.6950   |
| 0.8341        | 0.26  | 100  | 0.8838          | 0.7385   | 0.6814   | 0.7385   |
| 0.7773        | 0.39  | 150  | 0.7473          | 0.7833   | 0.7340   | 0.7833   |
| 0.7188        | 0.53  | 200  | 0.7024          | 0.7925   | 0.7433   | 0.7925   |
| 0.7483        | 0.66  | 250  | 0.7056          | 0.7872   | 0.7396   | 0.7872   |
| 0.6228        | 0.79  | 300  | 0.6338          | 0.8129   | 0.7636   | 0.8129   |
| 0.7089        | 0.92  | 350  | 0.6130          | 0.8208   | 0.7963   | 0.8208   |
| 0.5055        | 1.05  | 400  | 0.5939          | 0.8300   | 0.8075   | 0.8300   |
| 0.3942        | 1.18  | 450  | 0.6021          | 0.8241   | 0.7916   | 0.8241   |
| 0.4248        | 1.32  | 500  | 0.5956          | 0.8300   | 0.8060   | 0.8300   |
| 0.3595        | 1.45  | 550  | 0.6173          | 0.8175   | 0.7897   | 0.8175   |
| 0.5263        | 1.58  | 600  | 0.6170          | 0.8162   | 0.7908   | 0.8162   |
| 0.5153        | 1.71  | 650  | 0.6007          | 0.8327   | 0.8043   | 0.8327   |
| 0.4237        | 1.84  | 700  | 0.5565          | 0.8399   | 0.8113   | 0.8399   |
| 0.3852        | 1.97  | 750  | 0.5631          | 0.8439   | 0.8146   | 0.8439   |
| 0.1916        | 2.11  | 800  | 0.5848          | 0.8439   | 0.8132   | 0.8439   |
| 0.2108        | 2.24  | 850  | 0.6054          | 0.8432   | 0.8094   | 0.8432   |
| 0.1752        | 2.37  | 900  | 0.6142          | 0.8439   | 0.8131   | 0.8439   |
| 0.1502        | 2.5   | 950  | 0.6100          | 0.8452   | 0.8119   | 0.8452   |
| 0.2253        | 2.63  | 1000 | 0.6084          | 0.8439   | 0.8228   | 0.8439   |
| 0.2193        | 2.76  | 1050 | 0.6062          | 0.8485   | 0.8171   | 0.8485   |
| 0.2182        | 2.89  | 1100 | 0.5966          | 0.8498   | 0.8182   | 0.8498   |


### Framework versions

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