README.md

---
license: mit
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
language:
- en
library_name: transformers
tags:
- Roberta
- Sentiment Analysis
widget:
- text: This product is really great!
- text: This product is really bad!
---

# 🌟 Fine-tuned RoBERTa for Sentiment Analysis on 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


### 🏋️ Training Details

- **📉 Eval Loss:** 0.1049
- **⏱️ Eval Runtime:** 3177.538 seconds
- **📈 Eval Samples/Second:** 226.591
- **🌀 Eval Steps/Second:** 7.081
- **🔄 Epoch:** 3.0
- **🏃 Train Runtime:** 110070.6349 seconds
- **📊 Train Samples/Second:** 78.495
- **🌀 Train Steps/Second:** 2.453
- **📉 Train Loss:** 0.0858
- **⏳ Eval Accuracy:** 97.19%
- **🌀 Eval Precision:** 97.9%
- **⏱️ Eval Recall:** 97.18%
- **📈 Eval F1 Score:** 97.19%


## 🚀 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) # 1 for positive, 0 for negative

# Get sentiment
logits = outputs.logits
print(logits)
```


## 📜 License

This model is licensed under the [MIT License](LICENSE).