---
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: []
---

<!-- 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 a truncated IMDB dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7208
- Accuracy: {'accuracy': 0.876}

## Model description

The purpose of this model is to turn distilbert into a sentiment classification model.

### 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy            |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log        | 1.0   | 250  | 2.0890          | {'accuracy': 0.862} |
| 0.2005        | 2.0   | 500  | 1.8919          | {'accuracy': 0.874} |
| 0.2005        | 3.0   | 750  | 1.7205          | {'accuracy': 0.871} |
| 0.0963        | 4.0   | 1000 | 1.7208          | {'accuracy': 0.876} |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1