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

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# empire-content-distilbert

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0786
- Accuracy: 0.9814
- F1 Macro: 0.9817

## 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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.4091        | 1.0   | 133  | 0.1518          | 0.9654   | 0.9630   |
| 0.0850        | 2.0   | 266  | 0.0976          | 0.9761   | 0.9757   |
| 0.0382        | 3.0   | 399  | 0.0757          | 0.9787   | 0.9780   |
| 0.0209        | 4.0   | 532  | 0.0786          | 0.9814   | 0.9817   |
| 0.0124        | 5.0   | 665  | 0.0842          | 0.9787   | 0.9790   |


### Framework versions

- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2