---
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: my_awesome_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: test
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.774
---

<!-- 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. -->

# my_awesome_model

This model is a fine-tuned version of [](https://huggingface.co/) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6804
- Accuracy: 0.774

## 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: 5e-05
- train_batch_size: 32
- 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: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6145        | 1.0   | 500  | 1.5603          | 0.3475   |
| 1.4622        | 2.0   | 1000 | 1.3149          | 0.4      |
| 1.2777        | 3.0   | 1500 | 1.2142          | 0.4365   |
| 1.1218        | 4.0   | 2000 | 1.0095          | 0.5915   |
| 0.8886        | 5.0   | 2500 | 0.8772          | 0.695    |
| 0.7649        | 6.0   | 3000 | 0.7803          | 0.7355   |
| 0.6818        | 7.0   | 3500 | 0.7220          | 0.7615   |
| 0.6268        | 8.0   | 4000 | 0.6870          | 0.773    |
| 0.5922        | 9.0   | 4500 | 0.6859          | 0.771    |
| 0.5658        | 10.0  | 5000 | 0.6804          | 0.774    |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1