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