---
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: 0.0000
- Accuracy: {'accuracy': 1.0}

## 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   | 171  | 0.0001          | {'accuracy': 1.0} |
| No log        | 2.0   | 342  | 0.0001          | {'accuracy': 1.0} |
| 0.0245        | 3.0   | 513  | 0.0001          | {'accuracy': 1.0} |
| 0.0245        | 4.0   | 684  | 0.0000          | {'accuracy': 1.0} |
| 0.0245        | 5.0   | 855  | 0.0001          | {'accuracy': 1.0} |
| 0.0           | 6.0   | 1026 | 0.0000          | {'accuracy': 1.0} |
| 0.0           | 7.0   | 1197 | 0.0000          | {'accuracy': 1.0} |
| 0.0           | 8.0   | 1368 | 0.0000          | {'accuracy': 1.0} |
| 0.0           | 9.0   | 1539 | 0.0000          | {'accuracy': 1.0} |
| 0.0           | 10.0  | 1710 | 0.0000          | {'accuracy': 1.0} |


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2