---
base_model: distilbert-base-uncased
datasets:
- shawhin/imdb-truncated
language:
- en
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
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 shawhin/imdb-truncated dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1124
- Accuracy: {'accuracy': 0.873}

## 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  | 0.7531          | {'accuracy': 0.822} |
| 0.4366        | 2.0   | 500  | 0.5896          | {'accuracy': 0.85}  |
| 0.4366        | 3.0   | 750  | 0.6032          | {'accuracy': 0.891} |
| 0.2163        | 4.0   | 1000 | 0.6212          | {'accuracy': 0.892} |
| 0.2163        | 5.0   | 1250 | 0.6968          | {'accuracy': 0.882} |
| 0.0917        | 6.0   | 1500 | 0.8690          | {'accuracy': 0.886} |
| 0.0917        | 7.0   | 1750 | 0.9716          | {'accuracy': 0.875} |
| 0.0131        | 8.0   | 2000 | 1.0623          | {'accuracy': 0.877} |
| 0.0131        | 9.0   | 2250 | 1.0750          | {'accuracy': 0.874} |
| 0.0043        | 10.0  | 2500 | 1.1124          | {'accuracy': 0.873} |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1