---
library_name: peft
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- base_model:adapter:distilbert-base-uncased
- lora
- transformers
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-base-category-classifier
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-category-classifier

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: 0.1482
- Accuracy: 0.9518
- Precision: 0.9514
- Recall: 0.9518
- F1: 0.9515

## 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: 16
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5032        | 1.0   | 1342 | 0.2337          | 0.9277   | 0.9265    | 0.9277 | 0.9267 |
| 0.1954        | 2.0   | 2684 | 0.1623          | 0.9490   | 0.9487    | 0.9490 | 0.9487 |
| 0.154         | 3.0   | 4026 | 0.1482          | 0.9518   | 0.9514    | 0.9518 | 0.9515 |


### Framework versions

- PEFT 0.17.1
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4