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---
library_name: peft
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: []
datasets:
- stanfordnlp/imdb
pipeline_tag: text-classification
---
<!-- 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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.898124
- Accuracy: {'accuracy': 0.893}
## Model description
Using LoRA to fine-tune distilbert/distilbert-base-uncased to classify movie reviews
## Training and evaluation data
https://huggingface.co/datasets/stanfordnlp/imdb
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.7278 | {'accuracy': 0.833} |
| 0.358 | 2.0 | 500 | 0.6268 | {'accuracy': 0.852} |
| 0.358 | 3.0 | 750 | 0.6568 | {'accuracy': 0.872} |
| 0.1873 | 4.0 | 1000 | 0.7663 | {'accuracy': 0.883} |
| 0.1873 | 5.0 | 1250 | 0.7704 | {'accuracy': 0.877} |
| 0.0437 | 6.0 | 1500 | 0.8981 | {'accuracy': 0.893} |
| 0.0437 | 7.0 | 1750 | 0.9872 | {'accuracy': 0.886} |
| 0.0148 | 8.0 | 2000 | 1.0022 | {'accuracy': 0.888} |
| 0.0148 | 9.0 | 2250 | 1.0471 | {'accuracy': 0.892} |
| 0.0006 | 10.0 | 2500 | 1.0335 | {'accuracy': 0.889} |
### Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0 |