transformers pytorch tensorboard safetensors distilbert text-classification generated_from_trainer dataset:emotion base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: emotion-advance-classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9325
      name: Accuracy
    - type: f1
      value: 0.9327944704708324
      name: F1
---

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# emotion-advance-classifier

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: 0.1684
- Accuracy: 0.9325
- F1: 0.9328

This model is trained and evaluated using 'emotion' dataset. 
A great dataset from an article that explored how emotions are represented in English Twitter messages. Unlike most sentiment analysis datasets that involve just “positive” and “negative” polarities, this dataset con‐ tains six basic emotions: anger, love, fear, joy, sadness, and surprise.

### 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1757        | 1.0   | 250  | 0.1891          | 0.93     | 0.9309 |
| 0.115         | 2.0   | 500  | 0.1684          | 0.9325   | 0.9328 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
