---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flan_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. -->

# flan_t5_base_amazon

This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5448
- Accuracy: 0.8412
- F1 Macro: 0.8142
- F1 Micro: 0.8412

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.1669        | 0.13  | 50   | 0.9142          | 0.7404   | 0.6916   | 0.7404   |
| 0.8536        | 0.26  | 100  | 0.8417          | 0.7569   | 0.7197   | 0.7569   |
| 0.827         | 0.39  | 150  | 0.6893          | 0.7905   | 0.7471   | 0.7905   |
| 0.672         | 0.53  | 200  | 0.7235          | 0.7984   | 0.7730   | 0.7984   |
| 0.7424        | 0.66  | 250  | 0.6684          | 0.7945   | 0.7461   | 0.7945   |
| 0.6802        | 0.79  | 300  | 0.6008          | 0.8215   | 0.8014   | 0.8215   |
| 0.7847        | 0.92  | 350  | 0.6225          | 0.8123   | 0.7925   | 0.8123   |
| 0.5258        | 1.05  | 400  | 0.6656          | 0.8215   | 0.8000   | 0.8215   |
| 0.4945        | 1.18  | 450  | 0.6410          | 0.8235   | 0.7983   | 0.8235   |
| 0.4097        | 1.32  | 500  | 0.5937          | 0.8347   | 0.8110   | 0.8347   |
| 0.4116        | 1.45  | 550  | 0.5966          | 0.8314   | 0.8061   | 0.8314   |
| 0.4785        | 1.58  | 600  | 0.5696          | 0.8347   | 0.8107   | 0.8347   |
| 0.4821        | 1.71  | 650  | 0.5536          | 0.8366   | 0.8098   | 0.8366   |
| 0.4137        | 1.84  | 700  | 0.5612          | 0.8373   | 0.8116   | 0.8373   |
| 0.4623        | 1.97  | 750  | 0.5448          | 0.8412   | 0.8142   | 0.8412   |
| 0.1953        | 2.11  | 800  | 0.5984          | 0.8472   | 0.8201   | 0.8472   |
| 0.2114        | 2.24  | 850  | 0.6189          | 0.8432   | 0.8177   | 0.8432   |
| 0.2252        | 2.37  | 900  | 0.6411          | 0.8465   | 0.8199   | 0.8465   |
| 0.1937        | 2.5   | 950  | 0.6044          | 0.8524   | 0.8245   | 0.8524   |
| 0.2611        | 2.63  | 1000 | 0.6188          | 0.8472   | 0.8189   | 0.8472   |
| 0.3021        | 2.76  | 1050 | 0.6018          | 0.8472   | 0.8189   | 0.8472   |
| 0.2309        | 2.89  | 1100 | 0.5804          | 0.8478   | 0.8186   | 0.8478   |


### Framework versions

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