---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: distilbert-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.942
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-emotion

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1270
- Accuracy: 0.942

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 250  | 0.1759          | 0.9305   |
| 0.3324        | 2.0   | 500  | 0.1329          | 0.9355   |
| 0.3324        | 3.0   | 750  | 0.1270          | 0.942    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1