Update README.md

README.md

@@ -12,28 +12,41 @@ widget:
 - text: "This product is great!"
 ---
 
-# Fine-tuned RoBERTa for Sentiment Analysis on Amazon Reviews
+# 🌟 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 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).
+- **🆕 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
+## 🏋️ 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
+- **🔧 Learning Rate:** 2e-5
+- **📦 Batch Size:** 16
+- **⏳ Epochs:** 3
+- **⚖️ Weight Decay:** 0.01
+- **📅 Evaluation Strategy:** Epoch
 
-## Usage
+
+### 🏋️ 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
+
+## 🚀 Usage
 
 You can use this model directly with the Hugging Face `transformers` library:
 
@@ -47,7 +60,12 @@ tokenizer = RobertaTokenizer.from_pretrained(model_name)
 # Example usage
 inputs = tokenizer("This product is great!", return_tensors="pt")
 outputs = model(**inputs)
+
+# Get sentiment
+logits = outputs.logits
+print(logits)
 ```
-## License
 
-This model is licensed under the mit license
+## 📜 License
+
+This model is licensed under the [MIT License](LICENSE).