---
license: mit
base_model: prajjwal1/bert-mini
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: bert-mini-emotion_classifier
  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-mini-emotion_classifier

This model is a fine-tuned version of [prajjwal1/bert-mini](https://huggingface.co/prajjwal1/bert-mini) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0648
- F1: 0.9315
- Roc Auc: 0.9589
- Accuracy: 0.9224

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4176        | 0.1   | 500  | 0.2929          | 0.6755 | 0.7687  | 0.5550   |
| 0.2278        | 0.19  | 1000 | 0.1623          | 0.8931 | 0.9246  | 0.8630   |
| 0.1513        | 0.29  | 1500 | 0.1184          | 0.9185 | 0.9450  | 0.9022   |
| 0.1198        | 0.38  | 2000 | 0.0957          | 0.9274 | 0.9536  | 0.9197   |
| 0.1011        | 0.48  | 2500 | 0.0815          | 0.9306 | 0.9568  | 0.9230   |
| 0.0881        | 0.58  | 3000 | 0.0729          | 0.9320 | 0.9575  | 0.9237   |
| 0.0815        | 0.67  | 3500 | 0.0669          | 0.9337 | 0.9596  | 0.9256   |
| 0.0767        | 0.77  | 4000 | 0.0633          | 0.9346 | 0.9609  | 0.9260   |
| 0.0721        | 0.86  | 4500 | 0.0612          | 0.9333 | 0.9602  | 0.9233   |
| 0.071         | 0.96  | 5000 | 0.0601          | 0.9339 | 0.9607  | 0.9251   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0