transformers pytorch tensorboard gpt2 text-classification generated_from_trainer dataset:emotion license:mit model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- emotion
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: jq_emo_gpt
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.947
      name: Accuracy
---

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# jq_emo_gpt

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2536
- Accuracy: 0.947

## 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: 6400
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5109        | 1.0   | 16000 | 0.5014          | 0.929    |
| 0.3765        | 2.0   | 32000 | 0.3135          | 0.9385   |
| 0.2526        | 3.0   | 48000 | 0.2385          | 0.945    |
| 0.1952        | 4.0   | 64000 | 0.2536          | 0.947    |


### Framework versions

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