Create README.md

README.md

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+---
+language: en
+license: mit
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- emotional-intelligence
+- sentiment-analysis
+- workplace-emotions
+- distilbert
+---
+
+# EQ Detection Model
+
+A fine-tuned **DistilBERT** model for detecting emotional intelligence levels in workplace-focused text data.
+
+---
+
+## Model Description
+
+- **Task:** Text Classification  
+- **Model Type:** Emotional Intelligence (EQ) Detection  
+- **Base Model:** distilbert-base-uncased  
+- **Language:** English  
+- **Output Classes:** 3 (NEGATIVE / NEUTRAL / POSITIVE)  
+- **Training Dataset Size:** 2,796 workplace communication samples  
+
+The model is designed to evaluate emotional regulation, tone, and behavioral intelligence in professional communication.
+
+---
+
+## Label Schema
+
+| Label | ID | Description |
+|------|----|-------------|
+| NEGATIVE | 0 | Poor emotional regulation, negative or aggressive expressions |
+| NEUTRAL  | 1 | Emotionally neutral or factual statements |
+| POSITIVE | 2 | High emotional intelligence and constructive emotional behavior |
+
+---
+
+## Training Performance
+
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|------:|---------------|-----------------|----------|
+| 1 | 0.188500 | 0.147850 | 94.89% |
+| 2 | 0.055100 | 0.120229 | 96.39% |
+
+**Final Validation Accuracy:** **96.39%**
+
+---
+
+## Training Configuration
+
+- **Framework:** Hugging Face Transformers  
+- **Optimizer:** AdamW  
+- **Batch Size:** 16  
+- **Learning Rate:** 2e-5  
+- **Epochs:** 2  
+- **Max Sequence Length:** 128 tokens  
+
+---
+
+## Intended Use
+
+This model is intended for:
+
+- Workplace communication analysis  
+- Emotional intelligence assessment  
+- HR analytics and employee development  
+- Team interaction and behavioral insights  
+
+---
+
+## How to Use
+
+### Load the Model
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="sreenathsree1578/Eq_funetuned"
+)