LABEL_0 LABEL_1 LABEL_2 LABEL_3 LABEL_4 LABEL_5
---
license: apache-2.0
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
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.934
    - name: F1
      type: f1
      value: 0.9337817808480242
---

<!-- 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: 0.2155
- Accuracy: 0.934
- F1: 0.9338

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1768        | 1.0   | 250  | 0.1867          | 0.924    | 0.9235 |
| 0.1227        | 2.0   | 500  | 0.1588          | 0.934    | 0.9346 |
| 0.1031        | 3.0   | 750  | 0.1656          | 0.931    | 0.9306 |
| 0.0843        | 4.0   | 1000 | 0.1662          | 0.9395   | 0.9392 |
| 0.0662        | 5.0   | 1250 | 0.1714          | 0.9325   | 0.9326 |
| 0.0504        | 6.0   | 1500 | 0.1821          | 0.934    | 0.9338 |
| 0.0429        | 7.0   | 1750 | 0.2038          | 0.933    | 0.9324 |
| 0.0342        | 8.0   | 2000 | 0.2054          | 0.938    | 0.9379 |
| 0.0296        | 9.0   | 2250 | 0.2128          | 0.9345   | 0.9345 |
| 0.0211        | 10.0  | 2500 | 0.2155          | 0.934    | 0.9338 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
