---
base_model: distilbert/distilbert-base-uncased
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: news-category-classifier-distilbert
  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. -->

# news-category-classifier-distilbert

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: 0.1640
- Accuracy: 0.9474

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.3293        | 1.0   | 2289  | 0.9119   | 0.2599          |
| 0.0576        | 2.0   | 4578  | 0.9193   | 0.2425          |
| 0.4575        | 3.0   | 6867  | 0.9223   | 0.2401          |
| 0.0339        | 4.0   | 9156  | 0.9245   | 0.2353          |
| 0.0512        | 5.0   | 11445 | 0.9267   | 0.2367          |
| 0.3254        | 6.0   | 13734 | 0.9267   | 0.2367          |
| 0.5933        | 7.0   | 16023 | 0.9482   | 0.1654          |
| 0.136         | 8.0   | 18312 | 0.9482   | 0.1654          |
| 0.3128        | 9.0   | 20601 | 0.1640   | 0.9474          |
| 0.0458        | 10.0  | 22890 | 0.1640   | 0.9474          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1