README.md

# Model Card for BERT-base Sentiment Analysis Model

## Model Details
This model is a fine-tuned version of BERT-base for sentiment analysis tasks.

## Training Data
The model was trained on the Rotten Tomatoes dataset.

## Training Procedure
- **Learning Rate**: 2e-5
- **Epochs**: 3
- **Batch Size**: 16
가능하면 다른 사람들도 똑같은 버트모델, 로튼 토메토를 이용했을 때 재현가능하도록 하는 모든 하이퍼 파라미터들을 다 적어라

## How to Use 허깅 페이스 쓸 때 어떤 것을 쓰면 된다는 걸 알려주는 것
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")

input_text = "The movie was fantastic with a gripping storyline!"
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model(inputs)
print(outputs.logits)
```

## Evaluation 평가 결과
- **Accuracy**: 81.97%

## Limitations 약점은 뭐가 있다는 것
The model may generate biased or inappropriate content
due to the nature of the training data.
It is recommended to use the model with caution and apply necessary filters.

## Ethical Considerations
- **Bias**: The model may inherit biases present in the training data.
- **Misuse**: The model can be misused to generate misleading or harmful content.

## Copyright and License
This model is licensed under the MIT License.