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README.md

@@ -1,22 +1,50 @@
 ---
-{}
+tags:
+- text-classification
+library_name: transformers
+language: en
+license: apache-2.0
 ---
 
-# Customer Service Intelligence Model
+# Customer Service Intelligence Model v1.0.0
 
-## Model Details
-- Base Model: RoBERTa
-- Task: Multi-task Classification and Regression
-- Last Updated: 2025-01-07
-- Dataset Size: Updated with continuous training
+A multi-task model for customer service interaction analysis.
+
+## Model Description
+This model performs multiple classification tasks on customer service conversations:
+- Intent Classification
+- Topic Classification
+- Sentiment Analysis
+- Fallback Type Detection
+- Empathy Score Prediction
+- Listening Score Assessment
+
+## Performance Metrics
 
-## Training Tasks
-1. Intent Classification
-2. Topic Classification
-3. Sentiment Analysis
-4. Fallback Type Prediction
-5. Empathy Score Regression
-6. Listening Score Regression
+Latest evaluation results:
+- intent_accuracy: 0.038
+- sentiment_accuracy: 0.537
+- empathy_rmse: 0.331
+- listening_rmse: 0.485
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model = AutoModelForSequenceClassification.from_pretrained("raghavdw/cci-capstone")
+tokenizer = AutoTokenizer.from_pretrained("raghavdw/cci-capstone")
+
+text = "I need help with my flight booking"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+## Training Details
+- Base Model: RoBERTa
+- Total Labels: 38
+- Updated: 2025-01-07
+- Version: 1.0.0
 
 ## Label Maps
 ```json
@@ -69,11 +97,3 @@
   }
 }
 ```
-
-## Usage Example
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-model = AutoModelForSequenceClassification.from_pretrained("raghavdw/cci-capstone")
-tokenizer = AutoTokenizer.from_pretrained("raghavdw/cci-capstone")
-```