| # 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. | |