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
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: train[:2000]
      args: split
    metrics:
    - type: accuracy
      value: 0.86
      name: Accuracy
    - type: f1
      value: 0.857112157555706
      name: F1
---

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# distilbert-base-uncased-finetuned-emotion

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: 0.5889
- Accuracy: 0.86
- F1: 0.8571

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.2434        | 1.0   | 25   | 1.0831          | 0.605    | 0.4881 |
| 0.9611        | 2.0   | 50   | 0.9177          | 0.705    | 0.6476 |
| 0.7196        | 3.0   | 75   | 0.7741          | 0.735    | 0.7132 |
| 0.4981        | 4.0   | 100  | 0.6540          | 0.785    | 0.7715 |
| 0.3496        | 5.0   | 125  | 0.5721          | 0.83     | 0.8245 |
| 0.2435        | 6.0   | 150  | 0.5500          | 0.83     | 0.8266 |
| 0.1727        | 7.0   | 175  | 0.4813          | 0.86     | 0.8580 |
| 0.1253        | 8.0   | 200  | 0.5203          | 0.85     | 0.8479 |
| 0.0909        | 9.0   | 225  | 0.5427          | 0.83     | 0.8255 |
| 0.066         | 10.0  | 250  | 0.5391          | 0.845    | 0.8425 |
| 0.0474        | 11.0  | 275  | 0.5305          | 0.86     | 0.8575 |
| 0.0382        | 12.0  | 300  | 0.5264          | 0.855    | 0.8520 |
| 0.0306        | 13.0  | 325  | 0.5419          | 0.87     | 0.8673 |
| 0.0292        | 14.0  | 350  | 0.5868          | 0.855    | 0.8512 |
| 0.0224        | 15.0  | 375  | 0.5751          | 0.85     | 0.8464 |
| 0.0192        | 16.0  | 400  | 0.5837          | 0.85     | 0.8464 |
| 0.016         | 17.0  | 425  | 0.5852          | 0.855    | 0.8523 |
| 0.0151        | 18.0  | 450  | 0.5880          | 0.855    | 0.8523 |
| 0.0146        | 19.0  | 475  | 0.5912          | 0.86     | 0.8571 |
| 0.0131        | 20.0  | 500  | 0.5889          | 0.86     | 0.8571 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
