transformers 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
tags:
- generated_from_trainer
model-index:
- name: pp_distilbert_ft_emotions
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9275
      name: Accuracy
---

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# pp_distilbert_ft_emotions

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: 0.1493
- Accuracy: 0.9275

## 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: 5e-05
- train_batch_size: 80
- eval_batch_size: 80
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.25  | 50   | 0.7329          | 0.758    |
| No log        | 0.5   | 100  | 0.2915          | 0.9195   |
| No log        | 0.75  | 150  | 0.2150          | 0.927    |
| No log        | 1.0   | 200  | 0.1780          | 0.9285   |
| No log        | 1.25  | 250  | 0.1777          | 0.9295   |
| No log        | 1.5   | 300  | 0.1547          | 0.937    |
| No log        | 1.75  | 350  | 0.1467          | 0.935    |
| No log        | 2.0   | 400  | 0.1446          | 0.937    |
| No log        | 2.25  | 450  | 0.1482          | 0.934    |
| 0.3073        | 2.5   | 500  | 0.1335          | 0.9385   |
| 0.3073        | 2.75  | 550  | 0.1344          | 0.9415   |
| 0.3073        | 3.0   | 600  | 0.1229          | 0.9425   |
| 0.3073        | 3.25  | 650  | 0.1381          | 0.939    |
| 0.3073        | 3.5   | 700  | 0.1292          | 0.941    |
| 0.3073        | 3.75  | 750  | 0.1278          | 0.944    |
| 0.3073        | 4.0   | 800  | 0.1258          | 0.944    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
