Create README.md

README.md

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+---
+annotations_creators:
+- machine-generated
+language:
+- en
+language_creators:
+- found
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: CCI Dataset V2
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+- feature-extraction
+task_ids:
+- intent-classification
+- sentiment-classification
+- topic-classification
+- multi-class-classification
+- sentiment-analysis
+paperswithcode_id: null
+---
+
+# CCI Dataset V2
+
+## Dataset Description
+
+- **Repository:** raghavdw/cci-dataset-v2
+- **Point of Contact:** [Please add your contact information]
+
+### Dataset Summary
+
+The CCI (Customer Conversation Intelligence) Dataset V2 is a comprehensive collection of airline customer service interactions from Intelligent Virtual Assistant (IVA) conversations. The dataset contains 15,598 entries with rich annotations including intent prediction, sentiment analysis, empathy scoring, and conversation topic classification. Originally sourced from airline customer service interactions, this dataset provides valuable insights into how virtual assistants handle customer inquiries in the aviation sector.
+
+### Supported Tasks and Leaderboards
+
+- **Conversation Intent Classification**: Predict the intent behind customer utterances
+- **Sentiment Analysis**: Analyze the sentiment of customer messages
+- **Empathy Detection**: Score conversations based on demonstrated empathy
+- **Active Listening Assessment**: Evaluate the quality of listening in conversations
+- **Topic Classification**: Categorize conversations into specific topics
+- **RSIC (Response Style Intelligence Classification)**: Analyze and score conversation response styles
+
+### Languages
+
+The dataset primarily contains conversations in English (to be confirmed).
+
+### Dataset Structure
+
+The dataset is split into training (80%) and testing (20%) sets.
+
+#### Data Instances
+
+Each instance in the dataset represents a single utterance or conversation turn with the following features:
+
+```python
+{
+    'Index': int,  # Unique identifier
+    'Utterance': str,  # The actual conversation text
+    'Predicted_Intent': str,  # Classified intent of the utterance
+    'Intent_Score': float,  # Confidence score of intent prediction
+    'Sentiment': str,  # Analyzed sentiment
+    'empathy_score': int,  # Numerical score for empathy
+    'listening_score': int,  # Numerical score for active listening
+    'Topic': int,  # Numerical topic identifier
+    'Topic_Name': str,  # Name/description of the topic
+    'rsic_score': float,  # Response Style Intelligence Classification score
+    'RSICs': str,  # Response Style Intelligence Classification categories
+    'fallback_type': str  # Type of fallback response if applicable
+}
+```
+
+#### Data Fields
+
+| Field Name | Type | Description |
+|------------|------|-------------|
+| Index | Integer | Unique identifier for each entry |
+| Utterance | String | The actual text of the conversation turn |
+| Predicted_Intent | String | The predicted intent of the utterance |
+| Intent_Score | Float | Confidence score for the intent prediction (0-1) |
+| Sentiment | String | Sentiment classification of the utterance |
+| empathy_score | Integer | Numerical score indicating level of empathy |
+| listening_score | Integer | Numerical score indicating active listening quality |
+| Topic | Integer | Numerical identifier for conversation topic |
+| Topic_Name | String | Descriptive name of the conversation topic |
+| rsic_score | Float | Response Style Intelligence Classification score |
+| RSICs | String | RSIC categories/labels |
+| fallback_type | String | Type of fallback response if applicable |
+
+#### Data Splits
+
+The dataset is divided into:
+- Training set: 12,478 examples (80%)
+- Test set: 3,120 examples (20%)
+
+### Dataset Creation
+
+#### Curation Rationale
+
+This dataset is created as part of a capstone project for a certification program in MLOps that is building a customer intelligence platform that can be used
+customer service chatbots, BERT classifiers and analytics and insights platforms for ML training and customer service enhancements
+
+#### Source Data
+
+This dataset is derived from the "Relational Strategies in Customer Service (RSICS)" dataset originally published on Kaggle (https://www.kaggle.com/datasets/veeralakrishna/relational-strategies-in-customer-servicersics). The source data consists of airline IVA (Intelligent Virtual Assistant) conversations, providing a real-world context for customer service interactions in the aviation industry.
+
+The original dataset was specifically designed to study relational strategies in customer service contexts, with a focus on how virtual assistants handle various types of customer inquiries and situations in the airline industry. This makes it particularly valuable for developing and improving customer service AI systems in high-stakes service environments.
+
+#### Annotations
+
+- Annotations are spot checked by human evaluators for sentiment and predicted_intent primarily
+
+### Considerations for Using the Data
+
+#### Social Impact of Dataset
+
+This dataset can be used to improve customer service interactions by:
+- Enhancing empathy in automated responses
+- Improving intent recognition accuracy
+- Developing better conversation flow understanding
+- Training more effective customer service systems
+
+#### Discussion of Biases
+
+- TBD
+
+#### Other Known Limitations
+
+- The dataset may contain specific patterns or biases based on the source of customer service interactions
+- Intent and sentiment predictions are model-generated and may contain inherent biases or errors
+
+### Additional Information
+
+#### Dataset Curators
+
+[To be added: Information about the team or individuals who created and maintain this dataset]
+
+#### Licensing Information
+
+[To be added: License information for the dataset]
+
+#### Citation Information
+
+[To be added: How to cite this dataset in academic work]
+
+#### Contributions
+
+Thanks to [@raghavdw](https://huggingface.co/raghavdw) for adding this dataset.