pytorch tensorboard distilbert text-classification dataset:go_emotions transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-go_emotions_20220608_1
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: go_emotions
      type: go_emotions
      args: simplified
    metrics:
    - name: F1
      type: f1
      value: 0.5575026333429091
    - name: Accuracy
      type: accuracy
      value: 0.43641725027644673
---

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# distilbert-base-uncased-finetuned-go_emotions_20220608_1

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0857
- F1: 0.5575
- Roc Auc: 0.7242
- Accuracy: 0.4364

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.173         | 1.0   | 679  | 0.1074          | 0.4245 | 0.6455  | 0.2976   |
| 0.0989        | 2.0   | 1358 | 0.0903          | 0.5199 | 0.6974  | 0.3972   |
| 0.0865        | 3.0   | 2037 | 0.0868          | 0.5504 | 0.7180  | 0.4263   |
| 0.0806        | 4.0   | 2716 | 0.0860          | 0.5472 | 0.7160  | 0.4233   |
| 0.0771        | 5.0   | 3395 | 0.0857          | 0.5575 | 0.7242  | 0.4364   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
