---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: BERT_Emotions_tuned
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9295
---

<!-- 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_Emotions_tuned

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2033
- Accuracy: 0.9295

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.1   | 100  | 0.8098          | 0.7195   |
| No log        | 0.2   | 200  | 0.4054          | 0.882    |
| No log        | 0.3   | 300  | 0.4686          | 0.877    |
| No log        | 0.4   | 400  | 0.2850          | 0.909    |
| 0.5652        | 0.5   | 500  | 0.2673          | 0.92     |
| 0.5652        | 0.6   | 600  | 0.2474          | 0.9255   |
| 0.5652        | 0.7   | 700  | 0.1943          | 0.933    |
| 0.5652        | 0.8   | 800  | 0.1779          | 0.9315   |
| 0.5652        | 0.9   | 900  | 0.1720          | 0.939    |
| 0.2212        | 1.0   | 1000 | 0.1747          | 0.9375   |
| 0.2212        | 1.1   | 1100 | 0.1902          | 0.933    |
| 0.2212        | 1.2   | 1200 | 0.1540          | 0.941    |
| 0.2212        | 1.3   | 1300 | 0.1599          | 0.937    |
| 0.2212        | 1.4   | 1400 | 0.1533          | 0.944    |
| 0.1315        | 1.5   | 1500 | 0.1421          | 0.937    |
| 0.1315        | 1.6   | 1600 | 0.1549          | 0.941    |
| 0.1315        | 1.7   | 1700 | 0.1284          | 0.9435   |
| 0.1315        | 1.8   | 1800 | 0.1376          | 0.934    |
| 0.1315        | 1.9   | 1900 | 0.1197          | 0.943    |
| 0.1204        | 2.0   | 2000 | 0.1319          | 0.9385   |
| 0.1204        | 2.1   | 2100 | 0.1535          | 0.935    |
| 0.1204        | 2.2   | 2200 | 0.1488          | 0.943    |
| 0.1204        | 2.3   | 2300 | 0.1583          | 0.94     |
| 0.1204        | 2.4   | 2400 | 0.1426          | 0.9425   |
| 0.0913        | 2.5   | 2500 | 0.1554          | 0.9395   |
| 0.0913        | 2.6   | 2600 | 0.1458          | 0.944    |
| 0.0913        | 2.7   | 2700 | 0.1504          | 0.943    |
| 0.0913        | 2.8   | 2800 | 0.1621          | 0.9465   |
| 0.0913        | 2.9   | 2900 | 0.1521          | 0.944    |
| 0.0842        | 3.0   | 3000 | 0.1533          | 0.944    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2