---
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: 0.927
    - name: F1
      type: f1
      value: 0.9271664736493986
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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. The model is trained in Chapter 2: Text Classification in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/02_classification.ipynb).

It achieves the following results on the evaluation set:
- Loss: 0.2192
- Accuracy: 0.927
- F1: 0.9272

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8569        | 1.0   | 250  | 0.3386          | 0.894    | 0.8888 |
| 0.2639        | 2.0   | 500  | 0.2192          | 0.927    | 0.9272 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.0
- Tokenizers 0.10.3