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multi-class-classification

Text Classification
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multi-class-classification
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: multi-class-classification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.928
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: default
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9185
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.8738350796775306
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9185
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9179425177997311
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.8650962919021573
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9185
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9185
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.8692821860210945
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9185
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9181177508591364
      verified: true
    - name: loss
      type: loss
      value: 0.20905950665473938
      verified: true
    - name: matthews_correlation
      type: matthews_correlation
      value: 0.8920254536671932
      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. -->

# multi-class-classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2009
- Accuracy: 0.928

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2643        | 1.0   | 1000 | 0.2009          | 0.928    |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1