---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_0416
  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: 0.9355
    - name: F1
      type: f1
      value: 0.9354663445630791
    - name: Precision
      type: precision
      value: 0.9083088769303077
---

<!-- 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_emotion_ft_0416

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: 0.1489
- Accuracy: 0.9355
- F1: 0.9355
- Precision: 0.9083

## 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     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.7697        | 1.0   | 250  | 0.2589          | 0.9215   | 0.9218 | 0.8905    |
| 0.2077        | 2.0   | 500  | 0.1735          | 0.929    | 0.9293 | 0.9003    |
| 0.1397        | 3.0   | 750  | 0.1531          | 0.934    | 0.9346 | 0.8979    |
| 0.1136        | 4.0   | 1000 | 0.1489          | 0.9355   | 0.9355 | 0.9083    |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2