bert-base-sst-nlp

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

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.