Update README.md

README.md

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----
-license: mit
----
+license: mit
+datasets:
+- dair-ai/emotion
+language:
+- en
+library_name: transformers
+widget:
+- text: I am so happy with the results!
+- text: I am so pissed with the results!
+tags:
+- debarta
+- debarta-v3-small
+- emotions-classifier
+---
+
+# 🌟 Fast Emotion-X: Fine-tuned DeBERTa V3 Small Based Emotion Detection 🌟
+
+This is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) for emotion detection on the [dair-ai/emotion](https://huggingface.co/dair-ai/emotion) dataset.
+
+## 🚀 Overview
+
+Fast Emotion-X is a state-of-the-art emotion detection model fine-tuned from Microsoft's DeBERTa V3 Small model. Designed to accurately classify text into one of six emotional categories, Fast Emotion-X leverages the robust capabilities of DeBERTa and fine-tunes it on a comprehensive emotion dataset, ensuring high accuracy and reliability.
+
+## 📜 Model Details
+
+- **🆕 Model Name:** `AnkitAI/deberta-v3-small-base-emotions-classifier`
+- **🔗 Base Model:** `microsoft/deberta-v3-small`
+- **📊 Dataset:** [dair-ai/emotion](https://huggingface.co/dair-ai/emotion)
+- **⚙️ Fine-tuning:** This model was fine-tuned for emotion detection with a classification head for six emotional categories (anger, disgust, fear, joy, sadness, surprise).
+
+## 🏋️ Training
+
+The model was trained using the following parameters:
+
+- **🔧 Learning Rate:** 2e-5
+- **📦 Batch Size:** 4
+- **⚖️ Weight Decay:** 0.01
+- **📅 Evaluation Strategy:** Epoch
+
+### 🏋️ Training Details
+
+- **📉 Eval Loss:** 0.0858
+- **⏱️ Eval Runtime:** 110070.6349 seconds
+- **📈 Eval Samples/Second:** 78.495
+- **🌀 Eval Steps/Second:** 2.453
+- **📉 Train Loss:** 0.1049
+- **⏳ Eval Accuracy:** 94.6%
+- **🌀 Eval Precision:** 94.8%
+- **⏱️ Eval Recall:** 94.5%
+- **📈 Eval F1 Score:** 94.7%
+
+## 🚀 Usage
+
+You can use this model directly with the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "AnkitAI/deberta-v3-small-base-emotions-classifier"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example usage
+def predict_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predictions = logits.argmax(dim=1)
+    return predictions
+
+text = "I'm so happy with the results!"
+emotion = predict_emotion(text)
+print("Detected Emotion:", emotion)
+```
+
+## 📝 Emotion Labels
+- 😠 Anger
+- 🤢 Disgust
+- 😨 Fear
+- 😊 Joy
+- 😢 Sadness
+- 😲 Surprise
+
+
+## 📜 Model Card Data
+
+| Parameter                     | Value                     |
+|-------------------------------|---------------------------|
+| Model Name                    | microsoft/deberta-v3-small |
+| Training Dataset              | dair-ai/emotion           |
+| Number of Training Epochs     | 20                         |
+| Learning Rate                 | 2e-5                      |
+| Per Device Train Batch Size   | 4                         |
+| Evaluation Strategy           | Epoch                     |
+| Best Model Accuracy           | 94.6%                     |
+
+
+## 📜 License
+
+This model is licensed under the [MIT License](LICENSE).