---
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-emotion
  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: 0.935
    - name: F1
      type: f1
      value: 0.9351633564924363
---

<!-- 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-emotion

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: 0.1501
- Accuracy: 0.935
- F1: 0.9352

## 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: 256
- eval_batch_size: 256
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3032        | 1.0   | 63   | 0.8573          | 0.7235   | 0.6635 |
| 0.5915        | 2.0   | 126  | 0.3309          | 0.901    | 0.8998 |
| 0.2647        | 3.0   | 189  | 0.2051          | 0.9305   | 0.9304 |
| 0.1727        | 4.0   | 252  | 0.1764          | 0.933    | 0.9327 |
| 0.1431        | 5.0   | 315  | 0.1648          | 0.9325   | 0.9328 |
| 0.1198        | 6.0   | 378  | 0.1576          | 0.9355   | 0.9354 |
| 0.1075        | 7.0   | 441  | 0.1598          | 0.935    | 0.9351 |
| 0.0987        | 8.0   | 504  | 0.1528          | 0.9335   | 0.9340 |
| 0.0917        | 9.0   | 567  | 0.1513          | 0.9335   | 0.9335 |
| 0.0877        | 10.0  | 630  | 0.1501          | 0.935    | 0.9352 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1