# 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.