---
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
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: 1.3518
- Accuracy: {'accuracy': 0.892}

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy            |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log        | 1.0   | 250  | 0.5531          | {'accuracy': 0.844} |
| 0.4257        | 2.0   | 500  | 0.3913          | {'accuracy': 0.888} |
| 0.4257        | 3.0   | 750  | 0.6203          | {'accuracy': 0.865} |
| 0.2247        | 4.0   | 1000 | 0.6630          | {'accuracy': 0.884} |
| 0.2247        | 5.0   | 1250 | 0.8218          | {'accuracy': 0.885} |
| 0.0802        | 6.0   | 1500 | 0.9760          | {'accuracy': 0.866} |
| 0.0802        | 7.0   | 1750 | 0.9308          | {'accuracy': 0.882} |
| 0.0458        | 8.0   | 2000 | 1.0010          | {'accuracy': 0.884} |
| 0.0458        | 9.0   | 2250 | 1.2157          | {'accuracy': 0.884} |
| 0.0263        | 10.0  | 2500 | 1.2556          | {'accuracy': 0.89}  |
| 0.0263        | 11.0  | 2750 | 1.0911          | {'accuracy': 0.892} |
| 0.0244        | 12.0  | 3000 | 1.2507          | {'accuracy': 0.884} |
| 0.0244        | 13.0  | 3250 | 1.3437          | {'accuracy': 0.889} |
| 0.0239        | 14.0  | 3500 | 1.1973          | {'accuracy': 0.893} |
| 0.0239        | 15.0  | 3750 | 1.1784          | {'accuracy': 0.894} |
| 0.0006        | 16.0  | 4000 | 1.2430          | {'accuracy': 0.892} |
| 0.0006        | 17.0  | 4250 | 1.3177          | {'accuracy': 0.888} |
| 0.0018        | 18.0  | 4500 | 1.3294          | {'accuracy': 0.893} |
| 0.0018        | 19.0  | 4750 | 1.3637          | {'accuracy': 0.891} |
| 0.0025        | 20.0  | 5000 | 1.3518          | {'accuracy': 0.892} |


### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2