---
base_model: distilbert-base-uncased
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
should probably proofread and complete it, then remove this comment. -->

# distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2966
- Accuracy: {'accuracy': 0.886}

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy            |
|:-------------:|:-----:|:-----:|:---------------:|:-------------------:|
| No log        | 1.0   | 125   | 0.2972          | {'accuracy': 0.873} |
| No log        | 2.0   | 250   | 0.4349          | {'accuracy': 0.857} |
| No log        | 3.0   | 375   | 0.4850          | {'accuracy': 0.861} |
| 0.2757        | 4.0   | 500   | 0.4277          | {'accuracy': 0.865} |
| 0.2757        | 5.0   | 625   | 0.4342          | {'accuracy': 0.881} |
| 0.2757        | 6.0   | 750   | 0.4613          | {'accuracy': 0.88}  |
| 0.2757        | 7.0   | 875   | 0.6101          | {'accuracy': 0.879} |
| 0.1047        | 8.0   | 1000  | 0.6068          | {'accuracy': 0.877} |
| 0.1047        | 9.0   | 1125  | 0.6253          | {'accuracy': 0.878} |
| 0.1047        | 10.0  | 1250  | 0.6737          | {'accuracy': 0.89}  |
| 0.1047        | 11.0  | 1375  | 0.8528          | {'accuracy': 0.867} |
| 0.0462        | 12.0  | 1500  | 0.8829          | {'accuracy': 0.879} |
| 0.0462        | 13.0  | 1625  | 0.8560          | {'accuracy': 0.881} |
| 0.0462        | 14.0  | 1750  | 0.9111          | {'accuracy': 0.877} |
| 0.0462        | 15.0  | 1875  | 0.9331          | {'accuracy': 0.883} |
| 0.0329        | 16.0  | 2000  | 0.8129          | {'accuracy': 0.879} |
| 0.0329        | 17.0  | 2125  | 0.8663          | {'accuracy': 0.882} |
| 0.0329        | 18.0  | 2250  | 0.8163          | {'accuracy': 0.887} |
| 0.0329        | 19.0  | 2375  | 0.7679          | {'accuracy': 0.891} |
| 0.0188        | 20.0  | 2500  | 0.7408          | {'accuracy': 0.893} |
| 0.0188        | 21.0  | 2625  | 0.8557          | {'accuracy': 0.889} |
| 0.0188        | 22.0  | 2750  | 0.9201          | {'accuracy': 0.878} |
| 0.0188        | 23.0  | 2875  | 0.8839          | {'accuracy': 0.893} |
| 0.0078        | 24.0  | 3000  | 0.9388          | {'accuracy': 0.886} |
| 0.0078        | 25.0  | 3125  | 0.9004          | {'accuracy': 0.877} |
| 0.0078        | 26.0  | 3250  | 0.9489          | {'accuracy': 0.89}  |
| 0.0078        | 27.0  | 3375  | 1.0055          | {'accuracy': 0.88}  |
| 0.0241        | 28.0  | 3500  | 0.9758          | {'accuracy': 0.88}  |
| 0.0241        | 29.0  | 3625  | 1.0809          | {'accuracy': 0.876} |
| 0.0241        | 30.0  | 3750  | 1.0976          | {'accuracy': 0.858} |
| 0.0241        | 31.0  | 3875  | 1.1300          | {'accuracy': 0.859} |
| 0.0293        | 32.0  | 4000  | 1.1039          | {'accuracy': 0.869} |
| 0.0293        | 33.0  | 4125  | 0.9788          | {'accuracy': 0.875} |
| 0.0293        | 34.0  | 4250  | 1.0639          | {'accuracy': 0.873} |
| 0.0293        | 35.0  | 4375  | 1.2398          | {'accuracy': 0.866} |
| 0.0088        | 36.0  | 4500  | 1.1332          | {'accuracy': 0.874} |
| 0.0088        | 37.0  | 4625  | 1.1145          | {'accuracy': 0.877} |
| 0.0088        | 38.0  | 4750  | 1.1481          | {'accuracy': 0.867} |
| 0.0088        | 39.0  | 4875  | 1.3712          | {'accuracy': 0.87}  |
| 0.0054        | 40.0  | 5000  | 1.3314          | {'accuracy': 0.871} |
| 0.0054        | 41.0  | 5125  | 1.2189          | {'accuracy': 0.879} |
| 0.0054        | 42.0  | 5250  | 1.4673          | {'accuracy': 0.864} |
| 0.0054        | 43.0  | 5375  | 1.2771          | {'accuracy': 0.885} |
| 0.0097        | 44.0  | 5500  | 0.9926          | {'accuracy': 0.879} |
| 0.0097        | 45.0  | 5625  | 1.0428          | {'accuracy': 0.881} |
| 0.0097        | 46.0  | 5750  | 1.3764          | {'accuracy': 0.867} |
| 0.0097        | 47.0  | 5875  | 1.2730          | {'accuracy': 0.88}  |
| 0.0076        | 48.0  | 6000  | 1.3435          | {'accuracy': 0.895} |
| 0.0076        | 49.0  | 6125  | 1.4281          | {'accuracy': 0.883} |
| 0.0076        | 50.0  | 6250  | 1.4440          | {'accuracy': 0.874} |
| 0.0076        | 51.0  | 6375  | 1.5093          | {'accuracy': 0.88}  |
| 0.0113        | 52.0  | 6500  | 1.2309          | {'accuracy': 0.877} |
| 0.0113        | 53.0  | 6625  | 1.1447          | {'accuracy': 0.88}  |
| 0.0113        | 54.0  | 6750  | 1.1743          | {'accuracy': 0.877} |
| 0.0113        | 55.0  | 6875  | 1.4742          | {'accuracy': 0.867} |
| 0.0179        | 56.0  | 7000  | 1.2592          | {'accuracy': 0.882} |
| 0.0179        | 57.0  | 7125  | 1.2337          | {'accuracy': 0.889} |
| 0.0179        | 58.0  | 7250  | 1.1486          | {'accuracy': 0.894} |
| 0.0179        | 59.0  | 7375  | 1.1452          | {'accuracy': 0.89}  |
| 0.0059        | 60.0  | 7500  | 1.1572          | {'accuracy': 0.891} |
| 0.0059        | 61.0  | 7625  | 1.1582          | {'accuracy': 0.891} |
| 0.0059        | 62.0  | 7750  | 1.3938          | {'accuracy': 0.884} |
| 0.0059        | 63.0  | 7875  | 1.2767          | {'accuracy': 0.89}  |
| 0.0006        | 64.0  | 8000  | 1.2217          | {'accuracy': 0.89}  |
| 0.0006        | 65.0  | 8125  | 1.2232          | {'accuracy': 0.89}  |
| 0.0006        | 66.0  | 8250  | 1.2689          | {'accuracy': 0.894} |
| 0.0006        | 67.0  | 8375  | 1.2529          | {'accuracy': 0.894} |
| 0.0           | 68.0  | 8500  | 1.2292          | {'accuracy': 0.894} |
| 0.0           | 69.0  | 8625  | 1.2053          | {'accuracy': 0.893} |
| 0.0           | 70.0  | 8750  | 1.2587          | {'accuracy': 0.891} |
| 0.0           | 71.0  | 8875  | 1.2803          | {'accuracy': 0.89}  |
| 0.0005        | 72.0  | 9000  | 1.3449          | {'accuracy': 0.889} |
| 0.0005        | 73.0  | 9125  | 1.3193          | {'accuracy': 0.891} |
| 0.0005        | 74.0  | 9250  | 1.3032          | {'accuracy': 0.892} |
| 0.0005        | 75.0  | 9375  | 1.3586          | {'accuracy': 0.895} |
| 0.0006        | 76.0  | 9500  | 1.3457          | {'accuracy': 0.894} |
| 0.0006        | 77.0  | 9625  | 1.3742          | {'accuracy': 0.892} |
| 0.0006        | 78.0  | 9750  | 1.3986          | {'accuracy': 0.891} |
| 0.0006        | 79.0  | 9875  | 1.5180          | {'accuracy': 0.884} |
| 0.0022        | 80.0  | 10000 | 1.5658          | {'accuracy': 0.879} |
| 0.0022        | 81.0  | 10125 | 1.5500          | {'accuracy': 0.879} |
| 0.0022        | 82.0  | 10250 | 1.4174          | {'accuracy': 0.888} |
| 0.0022        | 83.0  | 10375 | 1.3601          | {'accuracy': 0.89}  |
| 0.0023        | 84.0  | 10500 | 1.4022          | {'accuracy': 0.887} |
| 0.0023        | 85.0  | 10625 | 1.3639          | {'accuracy': 0.89}  |
| 0.0023        | 86.0  | 10750 | 1.2567          | {'accuracy': 0.887} |
| 0.0023        | 87.0  | 10875 | 1.3608          | {'accuracy': 0.89}  |
| 0.0043        | 88.0  | 11000 | 1.3487          | {'accuracy': 0.888} |
| 0.0043        | 89.0  | 11125 | 1.3392          | {'accuracy': 0.889} |
| 0.0043        | 90.0  | 11250 | 1.3368          | {'accuracy': 0.888} |
| 0.0043        | 91.0  | 11375 | 1.3246          | {'accuracy': 0.881} |
| 0.0002        | 92.0  | 11500 | 1.3173          | {'accuracy': 0.881} |
| 0.0002        | 93.0  | 11625 | 1.2988          | {'accuracy': 0.888} |
| 0.0002        | 94.0  | 11750 | 1.3090          | {'accuracy': 0.882} |
| 0.0002        | 95.0  | 11875 | 1.3269          | {'accuracy': 0.894} |
| 0.0006        | 96.0  | 12000 | 1.2966          | {'accuracy': 0.885} |
| 0.0006        | 97.0  | 12125 | 1.2965          | {'accuracy': 0.885} |
| 0.0006        | 98.0  | 12250 | 1.2966          | {'accuracy': 0.886} |
| 0.0006        | 99.0  | 12375 | 1.2965          | {'accuracy': 0.886} |
| 0.0           | 100.0 | 12500 | 1.2966          | {'accuracy': 0.886} |


### Framework versions

- PEFT 0.11.1
- Transformers 4.43.1
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1