# 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

## How to Use
```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
- **Accuracy**: 81.97%

## Limitations
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.