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- vocab.json +0 -0
README.md
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# Customer Service Intelligence Model
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Updated: 2025-01-07 22:
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## Model
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- Base: RoBERTa
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- Using checkpoint: model.safetensors
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## Usage
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```python
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model = AutoModelForSequenceClassification.from_pretrained("raghavdw/cci-capstone")
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tokenizer = AutoTokenizer.from_pretrained("raghavdw/cci-capstone")
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# Example
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text = "I need help with my flight booking"
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model(**inputs)
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```
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## Label Maps
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```json
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{
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"Predicted_Intent": {
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"AccountAccess": 0,
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"BaggageClaim": 1,
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"CancelFlight": 2,
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"ChangeFlight": 3,
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"CheckFlightStatus": 4,
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"Complaint": 5,
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"CreditCardInquiry": 6,
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"GeneralInquiry": 7,
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"GetRefund": 8,
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"InFlightEntertainment": 9,
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"MileageInquiry": 10,
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"Other": 11,
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"PetTravel": 12,
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"ProvideInformation": 13,
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"RequestAssistance": 14,
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"UpgradeRequest": 15
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},
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"Topic_Name": {
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"Account Access": 0,
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"Baggage Claim": 1,
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"Cancel Flight": 2,
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"Change Flight": 3,
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"Check Flight Status": 4,
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"Complaint": 5,
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"Credit Card Inquiry": 6,
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"General Inquiry": 7,
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"Get Refund": 8,
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"In-Flight Entertainment": 9,
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"Mileage Inquiry": 10,
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"Other": 11,
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"Pet Travel": 12,
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"Provide Information": 13,
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"Request Assistance": 14,
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"Upgrade Request": 15
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},
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"Sentiment": {
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"NEGATIVE": 0,
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"POSITIVE": 1
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},
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"fallback_type": {
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"intent mismatch": 0,
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"no fallback": 1,
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"other fallback": 2,
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"unknown query": 3
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}
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}
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```
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# Customer Service Intelligence Model
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Last Updated: 2025-01-07 22:14:37
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Using Checkpoint: checkpoint-1170
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## Model Configuration
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- Base: RoBERTa
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- Multi-task Classification
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- Checkpoint: checkpoint-1170
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## Usage
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```python
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model = AutoModelForSequenceClassification.from_pretrained("raghavdw/cci-capstone")
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tokenizer = AutoTokenizer.from_pretrained("raghavdw/cci-capstone")
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text = "I need help with my flight booking"
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model(**inputs)
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```
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vocab.json
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