raghavdw
/

cci-capstone

@@ -1,199 +1,96 @@
----
-library_name: transformers
-tags: []
----
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+# Customer Service Intelligence Prediction Model
+## Model Overview
+- **Base Model**: RoBERTa
+- **Task**: Multi-task Classification and Regression
+- **Last Updated**: 2025-01-02
+## Prediction Targets and Ranges
+### 1. Predicted Intent
+Possible Values:
+[
+  "AccountAccess",
+  "BaggageClaim",
+  "CancelFlight",
+  "ChangeFlight",
+  "CheckFlightStatus",
+  "Complaint",
+  "CreditCardInquiry",
+  "GeneralInquiry",
+  "GetRefund",
+  "InFlightEntertainment",
+  "MileageInquiry",
+  "Other",
+  "PetTravel",
+  "ProvideInformation",
+  "RequestAssistance",
+  "UpgradeRequest"
+]
+### 2. Topic Name
+Possible Values:
+[
+  "Account Access",
+  "Baggage Claim",
+  "Cancel Flight",
+  "Change Flight",
+  "Check Flight Status",
+  "Complaint",
+  "Credit Card Inquiry",
+  "General Inquiry",
+  "Get Refund",
+  "In-Flight Entertainment",
+  "Mileage Inquiry",
+  "Other",
+  "Pet Travel",
+  "Provide Information",
+  "Request Assistance",
+  "Upgrade Request"
+]
+### 3. Sentiment
+Possible Values:
+[
+  "NEGATIVE",
+  "POSITIVE"
+]
+### 4. Fallback Type
+Possible Values:
+[
+  "intent mismatch",
+  "no fallback",
+  "other fallback",
+  "unknown query"
+]
+### 5. Empathy Score
+- **Type**: Regression
+- **Range**: 0.0 - 1.0 (normalized)
+- Represents the model's assessment of empathetic understanding
+### 6. Listening Score
+- **Type**: Regression
+- **Range**: 0.0 - 1.0 (normalized)
+- Indicates the level of attentive listening
+### 7. RSIC (Reason for Seeking Information or Support)
+- **Categories**: Low, Medium, High
+- **Score Range**: 1.0 - 5.0
+  - Low: 1.0 - 2.5
+  - Medium: 2.5 - 3.5
+  - High: 3.5 - 5.0
+## Usage Example
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+model = AutoModelForSequenceClassification.from_pretrained("raghavdw/cci-capstone")
+tokenizer = AutoTokenizer.from_pretrained("raghavdw/cci-capstone")
+## Performance Metrics
+- Trained on customer service interaction dataset
+- Multi-task learning approach
+- Captures nuanced customer communication signals