---
license: mit
tags:
- generated_from_trainer
datasets:
- go_emotions
metrics:
- f1
- accuracy
model-index:
- name: go_emo_gpt
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: go_emotions
      type: go_emotions
      config: simplified
      split: validation
      args: simplified
    metrics:
    - name: F1
      type: f1
      value: 0.6009707054948864
    - name: Accuracy
      type: accuracy
      value: 0.49963140434942865
---

<!-- 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. -->

# go_emo_gpt

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the go_emotions dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0964
- F1: 0.6010
- Roc Auc: 0.7659
- Accuracy: 0.4996

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: (13023,)
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step   | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|:--------:|
| 0.105         | 1.0   | 43410  | 0.0967          | 0.5795 | 0.7476  | 0.4757   |
| 0.0949        | 2.0   | 86820  | 0.0938          | 0.6012 | 0.7636  | 0.5035   |
| 0.0837        | 3.0   | 130230 | 0.0964          | 0.6010 | 0.7659  | 0.4996   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3