tensorboard safetensors generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 region:us
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
  results: []
---

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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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2719
- Accuracy: {'accuracy': 0.9169444444444445}

## 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: 2e-05
- 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy                         |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------------------:|
| 0.676         | 1.0   | 1050  | 0.5094          | {'accuracy': 0.8197222222222222} |
| 0.4394        | 2.0   | 2100  | 0.3866          | {'accuracy': 0.8675}             |
| 0.3705        | 3.0   | 3150  | 0.3472          | {'accuracy': 0.8822222222222222} |
| 0.3458        | 4.0   | 4200  | 0.3141          | {'accuracy': 0.8908333333333334} |
| 0.3287        | 5.0   | 5250  | 0.3063          | {'accuracy': 0.8977777777777778} |
| 0.2942        | 6.0   | 6300  | 0.2930          | {'accuracy': 0.9033333333333333} |
| 0.2735        | 7.0   | 7350  | 0.2864          | {'accuracy': 0.9091666666666667} |
| 0.2856        | 8.0   | 8400  | 0.2797          | {'accuracy': 0.9122222222222223} |
| 0.2826        | 9.0   | 9450  | 0.2800          | {'accuracy': 0.9113888888888889} |
| 0.2728        | 10.0  | 10500 | 0.2731          | {'accuracy': 0.9147222222222222} |
| 0.2674        | 11.0  | 11550 | 0.2763          | {'accuracy': 0.9136111111111112} |
| 0.2454        | 12.0  | 12600 | 0.2742          | {'accuracy': 0.915}              |
| 0.2661        | 13.0  | 13650 | 0.2716          | {'accuracy': 0.9177777777777778} |
| 0.2704        | 14.0  | 14700 | 0.2721          | {'accuracy': 0.9172222222222223} |
| 0.2735        | 15.0  | 15750 | 0.2719          | {'accuracy': 0.9169444444444445} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
