---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: jq_emo_distilbert
  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: 0.9385
---

<!-- 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. -->

# jq_emo_distilbert

This model is a fine-tuned version of [tingtone/jq_emo_distilbert](https://huggingface.co/tingtone/jq_emo_distilbert) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3185
- Accuracy: 0.9385

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1042        | 1.0   | 1000  | 0.1816          | 0.932    |
| 0.0998        | 2.0   | 2000  | 0.1799          | 0.934    |
| 0.0957        | 3.0   | 3000  | 0.2015          | 0.935    |
| 0.0846        | 4.0   | 4000  | 0.2129          | 0.9335   |
| 0.0943        | 5.0   | 5000  | 0.2215          | 0.935    |
| 0.075         | 6.0   | 6000  | 0.2627          | 0.9375   |
| 0.0607        | 7.0   | 7000  | 0.2908          | 0.9345   |
| 0.0636        | 8.0   | 8000  | 0.3207          | 0.935    |
| 0.0953        | 9.0   | 9000  | 0.3165          | 0.936    |
| 0.0748        | 10.0  | 10000 | 0.3185          | 0.9385   |


### Framework versions

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