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: DBERT_Emotions_tuned
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.925
      name: Accuracy
---

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# DBERT_Emotions_tuned

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: 0.1828
- Accuracy: 0.925

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.1   | 100  | 0.7513          | 0.7365   |
| No log        | 0.2   | 200  | 0.3693          | 0.8895   |
| No log        | 0.3   | 300  | 0.3118          | 0.906    |
| No log        | 0.4   | 400  | 0.3048          | 0.9055   |
| 0.5368        | 0.5   | 500  | 0.2649          | 0.9225   |
| 0.5368        | 0.6   | 600  | 0.2192          | 0.9235   |
| 0.5368        | 0.7   | 700  | 0.2254          | 0.9245   |
| 0.5368        | 0.8   | 800  | 0.2016          | 0.931    |
| 0.5368        | 0.9   | 900  | 0.1685          | 0.935    |
| 0.2254        | 1.0   | 1000 | 0.1926          | 0.9295   |
| 0.2254        | 1.1   | 1100 | 0.2128          | 0.928    |
| 0.2254        | 1.2   | 1200 | 0.2008          | 0.9325   |
| 0.2254        | 1.3   | 1300 | 0.1662          | 0.9385   |
| 0.2254        | 1.4   | 1400 | 0.1945          | 0.939    |
| 0.1315        | 1.5   | 1500 | 0.1652          | 0.939    |
| 0.1315        | 1.6   | 1600 | 0.1820          | 0.938    |
| 0.1315        | 1.7   | 1700 | 0.1660          | 0.938    |
| 0.1315        | 1.8   | 1800 | 0.1590          | 0.93     |
| 0.1315        | 1.9   | 1900 | 0.1601          | 0.935    |
| 0.1295        | 2.0   | 2000 | 0.1645          | 0.9345   |
| 0.1295        | 2.1   | 2100 | 0.1845          | 0.9305   |
| 0.1295        | 2.2   | 2200 | 0.1784          | 0.9355   |
| 0.1295        | 2.3   | 2300 | 0.2042          | 0.9365   |
| 0.1295        | 2.4   | 2400 | 0.1852          | 0.9365   |
| 0.0891        | 2.5   | 2500 | 0.1797          | 0.94     |
| 0.0891        | 2.6   | 2600 | 0.1741          | 0.9365   |
| 0.0891        | 2.7   | 2700 | 0.1758          | 0.9385   |
| 0.0891        | 2.8   | 2800 | 0.1771          | 0.944    |
| 0.0891        | 2.9   | 2900 | 0.1688          | 0.9385   |
| 0.0848        | 3.0   | 3000 | 0.1671          | 0.94     |


### Framework versions

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