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

# DistilBertLoRa

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the IMDB Movie dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0234
- Accuracy: {'accuracy': 0.884}

## 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  | 0.4076          | {'accuracy': 0.876} |
| 0.429         | 2.0   | 500  | 0.3507          | {'accuracy': 0.863} |
| 0.429         | 3.0   | 750  | 0.5018          | {'accuracy': 0.881} |
| 0.2304        | 4.0   | 1000 | 0.7036          | {'accuracy': 0.864} |
| 0.2304        | 5.0   | 1250 | 0.8113          | {'accuracy': 0.862} |
| 0.1136        | 6.0   | 1500 | 0.8428          | {'accuracy': 0.882} |
| 0.1136        | 7.0   | 1750 | 0.9134          | {'accuracy': 0.89}  |
| 0.0153        | 8.0   | 2000 | 0.9723          | {'accuracy': 0.884} |
| 0.0153        | 9.0   | 2250 | 1.0225          | {'accuracy': 0.884} |
| 0.0089        | 10.0  | 2500 | 1.0234          | {'accuracy': 0.884} |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1