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: 0.931
    - name: F1
      type: f1
      value: 0.9313235272564213
---

<!-- 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: 0.1595
- Accuracy: 0.931
- F1: 0.9313

## 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: 128
- eval_batch_size: 128
- 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 125  | 0.1873          | 0.924    | 0.9234 |
| 0.1992        | 2.0   | 250  | 0.1649          | 0.929    | 0.9293 |
| 0.1992        | 3.0   | 375  | 0.1595          | 0.931    | 0.9313 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0
- Datasets 2.2.3.dev0
- Tokenizers 0.12.1
