Update README.md

README.md

@@ -12,44 +12,42 @@ widget:
 - text: This product is really bad!
 ---
 
-# 🌟 Fine-tuned RoBERTa for Sentiment Analysis on Reviews 🌟
+# 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.
+This model 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
-- **⚖️ Weight Decay:** 0.01
-- **📅 Evaluation Strategy:** Epoch
+- **Learning Rate:** 2e-5
+- **Batch Size:** 16
+- **Weight Decay:** 0.01
+- **Evaluation Strategy:** Epoch
 
+### Training Details
 
-### 🏋️ 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%
 
-- **📉 Eval Loss:** 0.1049
-- **⏱️ Eval Runtime:** 3177.538 seconds
-- **📈 Eval Samples/Second:** 226.591
-- **🌀 Eval Steps/Second:** 7.081
-- **🏃 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
+## Usage
 
 You can use this model directly with the Hugging Face `transformers` library:
 
@@ -63,10 +61,8 @@ 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
-
 ```
 
+## License
 
-## 📜 License
-
-This model is licensed under the [MIT License](LICENSE).
+This model is licensed under the [MIT License](LICENSE).