---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-finetuned
  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: 0.9385
    - name: F1
      type: f1
      value: 0.9383538787245842
---

<!-- 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-finetuned

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: 0.1775
- Accuracy: 0.9385
- F1: 0.9384

## 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: 64
- eval_batch_size: 64
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 250  | 0.2451          | 0.9225   | 0.9227 |
| 0.4827        | 2.0   | 500  | 0.1655          | 0.934    | 0.9335 |
| 0.4827        | 3.0   | 750  | 0.1558          | 0.9365   | 0.9372 |
| 0.1191        | 4.0   | 1000 | 0.1482          | 0.9375   | 0.9374 |
| 0.1191        | 5.0   | 1250 | 0.1599          | 0.9365   | 0.9366 |
| 0.0775        | 6.0   | 1500 | 0.1539          | 0.9375   | 0.9378 |
| 0.0775        | 7.0   | 1750 | 0.1657          | 0.937    | 0.9366 |
| 0.0525        | 8.0   | 2000 | 0.1688          | 0.9385   | 0.9385 |
| 0.0525        | 9.0   | 2250 | 0.1811          | 0.9405   | 0.9406 |
| 0.0383        | 10.0  | 2500 | 0.1775          | 0.9385   | 0.9384 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1