---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: distilbert-base-uncased_emotion_ft_learn2pro
  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: 0.937
    - name: F1
      type: f1
      value: 0.9372926688327409
    - name: Precision
      type: precision
      value: 0.9097477369572983
---

<!-- 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_learn2pro

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: 0.1427
- Accuracy: 0.937
- F1: 0.9373
- Precision: 0.9097

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.7939        | 1.0   | 250  | 0.2551          | 0.9115   | 0.9095 | 0.8923    |
| 0.2063        | 2.0   | 500  | 0.1629          | 0.931    | 0.9310 | 0.9116    |
| 0.1384        | 3.0   | 750  | 0.1491          | 0.9375   | 0.9380 | 0.9073    |
| 0.1099        | 4.0   | 1000 | 0.1427          | 0.937    | 0.9373 | 0.9097    |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3