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

<!-- 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

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9069
- Accuracy: {'accuracy': 0.871}

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy            |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log        | 1.0   | 250  | 1.7205          | {'accuracy': 0.868} |
| 0.1563        | 2.0   | 500  | 1.5937          | {'accuracy': 0.865} |
| 0.1563        | 3.0   | 750  | 1.6187          | {'accuracy': 0.861} |
| 0.1939        | 4.0   | 1000 | 1.2535          | {'accuracy': 0.861} |
| 0.1939        | 5.0   | 1250 | 1.5725          | {'accuracy': 0.87}  |
| 0.0701        | 6.0   | 1500 | 1.7691          | {'accuracy': 0.874} |
| 0.0701        | 7.0   | 1750 | 1.8419          | {'accuracy': 0.864} |
| 0.0224        | 8.0   | 2000 | 1.8159          | {'accuracy': 0.876} |
| 0.0224        | 9.0   | 2250 | 1.9088          | {'accuracy': 0.871} |
| 0.0129        | 10.0  | 2500 | 1.9069          | {'accuracy': 0.871} |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1