---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: jq_emo_gpt
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.941
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# jq_emo_gpt

This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3379
- Accuracy: 0.941

## 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: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.7328        | 1.0   | 16000  | 0.6227          | 0.899    |
| 0.3989        | 2.0   | 32000  | 0.4351          | 0.927    |
| 0.2888        | 3.0   | 48000  | 0.3162          | 0.9385   |
| 0.2325        | 4.0   | 64000  | 0.2936          | 0.9445   |
| 0.2774        | 5.0   | 80000  | 0.2903          | 0.94     |
| 0.1423        | 6.0   | 96000  | 0.3410          | 0.9405   |
| 0.1681        | 7.0   | 112000 | 0.3259          | 0.9385   |
| 0.1743        | 8.0   | 128000 | 0.3225          | 0.9415   |
| 0.1011        | 9.0   | 144000 | 0.3356          | 0.942    |
| 0.1138        | 10.0  | 160000 | 0.3379          | 0.941    |


### Framework versions

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