---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-agnews-classification-fine-tune
  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-agnews-classification-fine-tune

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1979
- Accuracy: 0.9434
- F1: 0.9435

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
| 0.4206        | 0.0667 | 500   | 0.3065          | 0.9021   | 0.9019 |
| 0.2721        | 0.1333 | 1000  | 0.2626          | 0.9151   | 0.9149 |
| 0.2403        | 0.2    | 1500  | 0.2584          | 0.9192   | 0.9190 |
| 0.2314        | 0.2667 | 2000  | 0.2398          | 0.9267   | 0.9263 |
| 0.232         | 0.3333 | 2500  | 0.2190          | 0.9318   | 0.9319 |
| 0.246         | 0.4    | 3000  | 0.1979          | 0.9338   | 0.9340 |
| 0.2092        | 0.4667 | 3500  | 0.2066          | 0.9309   | 0.9310 |
| 0.2171        | 0.5333 | 4000  | 0.2058          | 0.9353   | 0.9353 |
| 0.2102        | 0.6    | 4500  | 0.1999          | 0.9368   | 0.9370 |
| 0.2           | 0.6667 | 5000  | 0.1967          | 0.9363   | 0.9363 |
| 0.1952        | 0.7333 | 5500  | 0.2025          | 0.9358   | 0.9359 |
| 0.1963        | 0.8    | 6000  | 0.2062          | 0.9374   | 0.9375 |
| 0.2025        | 0.8667 | 6500  | 0.1918          | 0.9386   | 0.9388 |
| 0.1839        | 0.9333 | 7000  | 0.1943          | 0.9413   | 0.9414 |
| 0.2008        | 1.0    | 7500  | 0.1766          | 0.9420   | 0.9420 |
| 0.1467        | 1.0667 | 8000  | 0.1948          | 0.9426   | 0.9426 |
| 0.1502        | 1.1333 | 8500  | 0.1960          | 0.9413   | 0.9414 |
| 0.1331        | 1.2    | 9000  | 0.1977          | 0.9443   | 0.9444 |
| 0.1421        | 1.2667 | 9500  | 0.2006          | 0.9428   | 0.9428 |
| 0.1375        | 1.3333 | 10000 | 0.1931          | 0.9437   | 0.9437 |
| 0.1375        | 1.4    | 10500 | 0.1979          | 0.9434   | 0.9435 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1