sadness joy love anger fear surprise
---
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: bertweet-base-finetuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9365
    - name: F1
      type: f1
      value: 0.9371
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.923
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8676576686813523
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.923
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9268406401714973
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8945488803260702
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.923
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.923
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8798961895301041
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.923
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9241278880972197
      verified: true
    - name: loss
      type: loss
      value: 0.24626904726028442
      verified: true
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1995
- Accuracy: 0.9365
- F1: 0.9371

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.475         | 1.0   | 503  | 0.2171          | 0.928    | 0.9292 |
| 0.1235        | 2.0   | 1006 | 0.1764          | 0.9365   | 0.9372 |
| 0.0802        | 3.0   | 1509 | 0.1788          | 0.938    | 0.9388 |
| 0.0531        | 4.0   | 2012 | 0.2005          | 0.938    | 0.9388 |
| 0.0367        | 5.0   | 2515 | 0.1995          | 0.9365   | 0.9371 |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
