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---
license: apache-2.0
library_name: peft
widget:
- text: "It was good."
example_title: "Positive"
- text: "Not a fan, don't recommed."
example_title: "Negative"
- text: "Better than the first one."
example_title: "Positive"
- text: "This is not worth watching even once. "
example_title: "Negative"
- text: "This one is a pass."
example_title: "Positive"
tags:
- sentiment
datasets:
- imdb
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 the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4297
- Accuracy: {'accuracy': 0.86}
## 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: 0.01
- 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: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------------------:|
| 0.7852 | 1.0 | 5000 | 0.4518 | {'accuracy': 0.8486} |
| 0.6289 | 2.0 | 10000 | 0.4492 | {'accuracy': 0.8528} |
| 0.0503 | 3.0 | 15000 | 0.4297 | {'accuracy': 0.86} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure
### Framework versions
- PEFT 0.6.2
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