Fine-tuned RoBERTa for Sentiment Analysis on Reviews

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on the Amazon Reviews dataset for sentiment analysis.

Model Details

• Model Name: AnkitAI/reviews-roberta-base-sentiment-analysis
• Base Model: cardiffnlp/twitter-roberta-base-sentiment-latest
• Dataset: Amazon Reviews
• Fine-tuning: This model was fine-tuned for sentiment analysis with a classification head for binary sentiment classification (positive and negative).

Training

The model was trained using the following parameters:

• Learning Rate: 2e-5
• Batch Size: 16
• Weight Decay: 0.01
• Evaluation Strategy: Epoch

Training Details

• Evaluation Loss: 0.1049
• Evaluation Runtime: 3177.538 seconds
• Evaluation Samples/Second: 226.591
• Evaluation Steps/Second: 7.081
• Training Runtime: 110070.6349 seconds
• Training Samples/Second: 78.495
• Training Steps/Second: 2.453
• Training Loss: 0.0858
• Evaluation Accuracy: 97.19%
• Evaluation Precision: 97.9%
• Evaluation Recall: 97.18%
• Evaluation F1 Score: 97.19%

Usage

You can use this model directly with the Hugging Face transformers library:

from transformers import RobertaForSequenceClassification, RobertaTokenizer

model_name = "AnkitAI/reviews-roberta-base-sentiment-analysis"
model = RobertaForSequenceClassification.from_pretrained(model_name)
tokenizer = RobertaTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("This product is great!", return_tensors="pt")
outputs = model(**inputs) # 1 for positive, 0 for negative

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License

This model is licensed under the MIT License.

More models: ankitaglawe.com