transformers 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: 0.936
      name: Accuracy
    - type: f1
      value: 0.9361019232595149
      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: 0.1538
- Accuracy: 0.936
- F1: 0.9361

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 63   | 0.8113          | 0.745    | 0.6916 |
| No log        | 2.0   | 126  | 0.2909          | 0.911    | 0.9107 |
| No log        | 3.0   | 189  | 0.2003          | 0.928    | 0.9277 |
| 0.5629        | 4.0   | 252  | 0.1698          | 0.938    | 0.9376 |
| 0.5629        | 5.0   | 315  | 0.1561          | 0.9365   | 0.9364 |
| 0.5629        | 6.0   | 378  | 0.1531          | 0.933    | 0.9334 |
| 0.5629        | 7.0   | 441  | 0.1584          | 0.9355   | 0.9345 |
| 0.1065        | 8.0   | 504  | 0.1493          | 0.9325   | 0.9321 |
| 0.1065        | 9.0   | 567  | 0.1504          | 0.936    | 0.9364 |
| 0.1065        | 10.0  | 630  | 0.1481          | 0.9395   | 0.9395 |
| 0.1065        | 11.0  | 693  | 0.1501          | 0.935    | 0.9353 |
| 0.0684        | 12.0  | 756  | 0.1504          | 0.936    | 0.9360 |
| 0.0684        | 13.0  | 819  | 0.1526          | 0.935    | 0.9352 |
| 0.0684        | 14.0  | 882  | 0.1526          | 0.9355   | 0.9357 |
| 0.0684        | 15.0  | 945  | 0.1538          | 0.936    | 0.9361 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
