pytorch tensorboard roberta text-classification dataset:emotion transformers generated_from_trainer model-index
---
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: 0.929
    - name: F1
      type: f1
      value: 0.9295613935787139
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.925
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8722017563353339
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.925
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9283646705517916
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8982480793145559
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.925
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.925
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.883488774573809
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.925
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9259820821054494
      verified: true
    - name: loss
      type: loss
      value: 0.18158096075057983
      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. -->

# bertweet-base-finetuned-emotion

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1737
- Accuracy: 0.929
- F1: 0.9296

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9469        | 1.0   | 250  | 0.3643          | 0.895    | 0.8921 |
| 0.2807        | 2.0   | 500  | 0.2173          | 0.9245   | 0.9252 |
| 0.1749        | 3.0   | 750  | 0.1859          | 0.926    | 0.9266 |
| 0.1355        | 4.0   | 1000 | 0.1737          | 0.929    | 0.9296 |


### Framework versions

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