---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: EmotiNet
  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. -->

# EmotiNet

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3224
- Accuracy: 0.9242
- Precision: 0.8830
- Recall: 0.8990
- F1: 0.8902

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1494        | 1.0   | 1500 | 0.3036          | 0.9237   | 0.8783    | 0.9166 | 0.8921 |
| 0.1481        | 2.0   | 3000 | 0.2944          | 0.9242   | 0.8893    | 0.8867 | 0.8877 |
| 0.072         | 3.0   | 4500 | 0.3224          | 0.9242   | 0.8830    | 0.8990 | 0.8902 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.2