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

# emotions_bert

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5151
- F1 Micro: 0.6887
- F1 Macro: 0.6024
- Accuracy: 0.1929

## 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 0.7549        | 0.4082 | 20   | 0.6455          | 0.6125   | 0.4264   | 0.1243   |
| 0.6144        | 0.8163 | 40   | 0.5675          | 0.6510   | 0.5188   | 0.1670   |
| 0.5496        | 1.2245 | 60   | 0.5414          | 0.6747   | 0.5570   | 0.1883   |
| 0.4878        | 1.6327 | 80   | 0.5191          | 0.6849   | 0.5894   | 0.2104   |
| 0.4754        | 2.0408 | 100  | 0.5140          | 0.6810   | 0.5909   | 0.2013   |
| 0.4027        | 2.4490 | 120  | 0.5169          | 0.6849   | 0.5880   | 0.2207   |
| 0.3986        | 2.8571 | 140  | 0.5151          | 0.6887   | 0.6024   | 0.1929   |
| 0.3711        | 3.2653 | 160  | 0.5187          | 0.6820   | 0.5991   | 0.2188   |
| 0.325         | 3.6735 | 180  | 0.5263          | 0.6753   | 0.5928   | 0.1942   |
| 0.3303        | 4.0816 | 200  | 0.5294          | 0.6900   | 0.5949   | 0.2149   |
| 0.2801        | 4.4898 | 220  | 0.5420          | 0.6840   | 0.5953   | 0.2097   |
| 0.2748        | 4.8980 | 240  | 0.5583          | 0.6797   | 0.5861   | 0.2162   |
| 0.2452        | 5.3061 | 260  | 0.5781          | 0.6758   | 0.5871   | 0.1981   |
| 0.2253        | 5.7143 | 280  | 0.5889          | 0.6715   | 0.5812   | 0.1929   |
| 0.226         | 6.1224 | 300  | 0.5955          | 0.6793   | 0.5852   | 0.2207   |
| 0.1958        | 6.5306 | 320  | 0.6120          | 0.6734   | 0.5861   | 0.2032   |
| 0.1952        | 6.9388 | 340  | 0.6209          | 0.6744   | 0.5806   | 0.2084   |
| 0.1758        | 7.3469 | 360  | 0.6339          | 0.6756   | 0.5789   | 0.2136   |
| 0.1691        | 7.7551 | 380  | 0.6412          | 0.6773   | 0.5779   | 0.2188   |
| 0.1613        | 8.1633 | 400  | 0.6431          | 0.6761   | 0.5794   | 0.2142   |
| 0.1486        | 8.5714 | 420  | 0.6532          | 0.6718   | 0.5763   | 0.2104   |
| 0.1529        | 8.9796 | 440  | 0.6577          | 0.6737   | 0.5747   | 0.2136   |
| 0.1436        | 9.3878 | 460  | 0.6658          | 0.6734   | 0.5744   | 0.2194   |
| 0.1399        | 9.7959 | 480  | 0.6640          | 0.6735   | 0.5745   | 0.2188   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1