---
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: 0.94
    - name: F1
      type: f1
      value: 0.9399689929524555
    - name: Precision
      type: precision
      value: 0.9171180948520368
---

<!-- 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: 0.1559
- Accuracy: 0.94
- F1: 0.9400
- Precision: 0.9171

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.7983        | 1.0   | 250  | 0.2761          | 0.91     | 0.9103 | 0.8773    |
| 0.2021        | 2.0   | 500  | 0.1690          | 0.935    | 0.9358 | 0.9022    |
| 0.1342        | 3.0   | 750  | 0.1606          | 0.9385   | 0.9386 | 0.9256    |
| 0.1034        | 4.0   | 1000 | 0.1471          | 0.937    | 0.9367 | 0.9236    |
| 0.0828        | 5.0   | 1250 | 0.1572          | 0.9355   | 0.9355 | 0.9132    |
| 0.0716        | 6.0   | 1500 | 0.1547          | 0.942    | 0.9415 | 0.9305    |
| 0.0595        | 7.0   | 1750 | 0.1584          | 0.9385   | 0.9385 | 0.9170    |
| 0.0514        | 8.0   | 2000 | 0.1559          | 0.94     | 0.9400 | 0.9171    |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2