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--- |
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license: mit |
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base_model: cardiffnlp/twitter-roberta-base-sentiment-latest |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- Roberta |
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- Sentiment Analysis |
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--- |
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# Fine-tuned RoBERTa for Sentiment Analysis on Amazon Reviews |
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This is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on the [Amazon Reviews dataset](https://www.kaggle.com/datasets/bittlingmayer/amazonreviews) for sentiment analysis. |
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## Model Details |
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- **Model Name:** AnkitAI/reviews-roberta-base-sentiment-analysis |
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- **Base Model:** cardiffnlp/twitter-roberta-base-sentiment-latest |
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- **Dataset:** [Amazon Reviews](https://www.kaggle.com/datasets/bittlingmayer/amazonreviews) |
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- **Fine-tuning:** This model was fine-tuned for sentiment analysis with a classification head for binary sentiment classification (positive and negative). |
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## Training |
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The model was trained using the following parameters: |
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- **Learning Rate:** 2e-5 |
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- **Batch Size:** 16 |
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- **Epochs:** 3 |
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- **Weight Decay:** 0.01 |
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- **Evaluation Strategy:** Epoch |
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## Usage |
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You can use this model directly with the Hugging Face `transformers` library: |
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```python |
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from transformers import RobertaForSequenceClassification, RobertaTokenizer |
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model_name = "AnkitAI/reviews-roberta-base-sentiment-analysis" |
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model = RobertaForSequenceClassification.from_pretrained(model_name) |
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tokenizer = RobertaTokenizer.from_pretrained(model_name) |
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# Example usage |
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inputs = tokenizer("This product is great!", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
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## License |
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This model is licensed under the mit license |
