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---
language: "en"
tags:
- distilbert
- sentiment
- positive
- negative
- review
- imdb
---
# Fine-tuned DistilBERT-base-uncased for IMDB Classification
# Model Description
DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on IMDB dataset with the purpose of classifying positive reviews from the bad ones. The model predicts these 2 classes.
The model is a fine-tuned version of [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert).
It was fine-tuned on IMDB dataset [https://huggingface.co/datasets/imdb].
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on IMDB dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2265
- Accuracy: 0.9312
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2273 | 1.0 | 1563 | 0.2471 | 0.9122 |
| 0.1524 | 2.0 | 3126 | 0.2265 | 0.9312 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
# How to Use
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="LukeGPT88/imdb_text_classifier")
classifier("I see it and it was awesome.")
```
```python
Output:
[{'label': 'POSITIVE', 'score': 0.9958052635192871}]
```
# Contact
Please reach out to [luca.flammia@gmail.com](luca.flammia@gmail.com) if you have any questions or feedback.
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