---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-lora-text-classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-lora-text-classification

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: 1.2300
- Accuracy: {'accuracy': 0.861}

## 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  | 0.5358          | {'accuracy': 0.853} |
| 0.398         | 2.0   | 500  | 0.5237          | {'accuracy': 0.866} |
| 0.398         | 3.0   | 750  | 0.6152          | {'accuracy': 0.849} |
| 0.1864        | 4.0   | 1000 | 0.7506          | {'accuracy': 0.855} |
| 0.1864        | 5.0   | 1250 | 0.9820          | {'accuracy': 0.867} |
| 0.0382        | 6.0   | 1500 | 1.0581          | {'accuracy': 0.856} |
| 0.0382        | 7.0   | 1750 | 1.1583          | {'accuracy': 0.862} |
| 0.0149        | 8.0   | 2000 | 1.2200          | {'accuracy': 0.858} |
| 0.0149        | 9.0   | 2250 | 1.2581          | {'accuracy': 0.862} |
| 0.006         | 10.0  | 2500 | 1.2300          | {'accuracy': 0.861} |


### Framework versions

- PEFT 0.8.1
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1