---
license: mit
pipeline_tag: text-classification
---

# Fine-tuned RoBERTa for Sentiment Analysis on Amazon Reviews

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.

## Model Details

- **Model Name:** AnkitAI/reviews-roberta-base-sentiment-analysis
- **Base Model:** cardiffnlp/twitter-roberta-base-sentiment-latest
- **Dataset:** [Amazon Reviews](https://www.kaggle.com/datasets/bittlingmayer/amazonreviews)
- **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
- **Epochs:** 3
- **Weight Decay:** 0.01
- **Evaluation Strategy:** Epoch

## Usage

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

```python
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)
```
## License

This model is licensed under the mit license