---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-multiclass-classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-finetuned-multiclass-classification

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6039
- Accuracy: 0.9045

## 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: 1.45e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 29
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.6788        | 1.0   | 2638  | 2.1418          | 0.7233   |
| 1.289         | 2.0   | 5276  | 1.0718          | 0.8385   |
| 0.7581        | 3.0   | 7914  | 0.7530          | 0.8878   |
| 0.5557        | 4.0   | 10552 | 0.6371          | 0.9007   |
| 0.493         | 5.0   | 13190 | 0.6039          | 0.9045   |


### Framework versions

- Transformers 4.41.2
- Pytorch 1.13.1+cpu
- Datasets 2.13.1
- Tokenizers 0.19.1