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: validation
      args: split
    metrics:
    - type: accuracy
      value: 0.9385
      name: Accuracy
    - type: f1
      value: 0.938496854642063
      name: F1
---

<!-- 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-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: 0.1910
- Accuracy: 0.9385
- F1: 0.9385

## 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: 256
- eval_batch_size: 256
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 63   | 0.3110          | 0.907    | 0.9066 |
| No log        | 2.0   | 126  | 0.2093          | 0.9255   | 0.9264 |
| No log        | 3.0   | 189  | 0.1683          | 0.931    | 0.9312 |
| 0.2667        | 4.0   | 252  | 0.1564          | 0.932    | 0.9319 |
| 0.2667        | 5.0   | 315  | 0.1541          | 0.9325   | 0.9328 |
| 0.2667        | 6.0   | 378  | 0.1577          | 0.9375   | 0.9378 |
| 0.2667        | 7.0   | 441  | 0.1547          | 0.9355   | 0.9357 |
| 0.0894        | 8.0   | 504  | 0.1528          | 0.9385   | 0.9386 |
| 0.0894        | 9.0   | 567  | 0.1630          | 0.9395   | 0.9394 |
| 0.0894        | 10.0  | 630  | 0.1745          | 0.9425   | 0.9427 |
| 0.0894        | 11.0  | 693  | 0.1635          | 0.9385   | 0.9385 |
| 0.0567        | 12.0  | 756  | 0.1706          | 0.938    | 0.9381 |
| 0.0567        | 13.0  | 819  | 0.1740          | 0.941    | 0.9413 |
| 0.0567        | 14.0  | 882  | 0.1766          | 0.94     | 0.9403 |
| 0.0567        | 15.0  | 945  | 0.1832          | 0.938    | 0.9382 |
| 0.0397        | 16.0  | 1008 | 0.1871          | 0.9385   | 0.9388 |
| 0.0397        | 17.0  | 1071 | 0.1889          | 0.938    | 0.9382 |
| 0.0397        | 18.0  | 1134 | 0.1908          | 0.935    | 0.9354 |
| 0.0397        | 19.0  | 1197 | 0.1907          | 0.94     | 0.9399 |
| 0.0284        | 20.0  | 1260 | 0.1910          | 0.9385   | 0.9385 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
