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AnkitAI/reviews-roberta-base-sentiment-analysis

🤗 Hugging Face sourcetext-classificationmit125M params499 MBsafetensors✓ 2 checksumsupdated today
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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

Support the Project

If this model is useful in your work, you can support independent research:

License

This model is licensed under the MIT License.

More models: ankitaglawe.com