---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_uncased_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. -->

# bert_base_uncased_amazon

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7916
- Accuracy: 0.7879
- F1 Macro: 0.7308
- F1 Micro: 0.7879

## 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: 2e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 2.5476        | 0.26  | 50   | 2.4071          | 0.4967   | 0.3546   | 0.4967   |
| 1.7401        | 0.53  | 100  | 1.6470          | 0.6337   | 0.4899   | 0.6337   |
| 1.3223        | 0.79  | 150  | 1.2889          | 0.6897   | 0.5665   | 0.6897   |
| 1.1317        | 1.05  | 200  | 1.1047          | 0.7358   | 0.6577   | 0.7358   |
| 0.9137        | 1.32  | 250  | 0.9907          | 0.7536   | 0.6820   | 0.7536   |
| 0.9434        | 1.58  | 300  | 0.9264          | 0.7602   | 0.6896   | 0.7602   |
| 0.86          | 1.84  | 350  | 0.8729          | 0.7780   | 0.7135   | 0.7780   |
| 0.7567        | 2.11  | 400  | 0.8322          | 0.7859   | 0.7244   | 0.7859   |
| 0.7028        | 2.37  | 450  | 0.8130          | 0.7892   | 0.7339   | 0.7892   |
| 0.6842        | 2.63  | 500  | 0.8005          | 0.7892   | 0.7284   | 0.7892   |
| 0.6784        | 2.89  | 550  | 0.7916          | 0.7879   | 0.7308   | 0.7879   |


### Framework versions

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