---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotions-dataset-2
  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: 0.937
    - name: F1
      type: f1
      value: 0.9369365562537507
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-emotions-dataset-2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2689
- Accuracy: 0.937
- F1: 0.9369

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6095        | 1.0   | 500  | 0.2145          | 0.9245   | 0.9250 |
| 0.1686        | 2.0   | 1000 | 0.1670          | 0.933    | 0.9316 |
| 0.1164        | 3.0   | 1500 | 0.1631          | 0.939    | 0.9394 |
| 0.0924        | 4.0   | 2000 | 0.1829          | 0.938    | 0.9366 |
| 0.072         | 5.0   | 2500 | 0.1929          | 0.9355   | 0.9354 |
| 0.0545        | 6.0   | 3000 | 0.2117          | 0.9355   | 0.9357 |
| 0.0432        | 7.0   | 3500 | 0.2222          | 0.934    | 0.9340 |
| 0.0298        | 8.0   | 4000 | 0.2553          | 0.939    | 0.9385 |
| 0.0248        | 9.0   | 4500 | 0.2667          | 0.936    | 0.9358 |
| 0.0199        | 10.0  | 5000 | 0.2689          | 0.937    | 0.9369 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0