| --- |
| dataset_info: |
| config_name: defualt |
| features: |
| - name: text |
| dtype: string |
| - name: inten |
| dtype: |
| class_label: |
| names: |
| '0': Make Appointment |
| '1': Bike Types |
| '2': Return Policy |
| '3': Fallback Intent |
| '4': Cost Estimation |
| '5': Welcome Intent |
| '6': Trade-in Options |
| '7': Hours |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train.csv |
| - split: test |
| path: test.csv |
| license: mit |
| task_categories: |
| - text-classification |
| language: |
| - en |
| tags: |
| - coffeshop |
| - customer |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
|
|
| **Dataset Card: Bike Shop Chat-bot Intents** |
|
|
| **Dataset Name:** Bike Shop Chat-bot Intents |
|
|
| **Description:** This dataset contains phrases labeled by intents, used to train and test a chat-bot for a bike shop. The intents represent the underlying goals or actions that users want to perform when interacting with the chat-bot. |
|
|
| **Files:** |
|
|
| * **intents_train.csv**: The training dataset, containing labeled phrases and their corresponding intents. |
| * **intents_test.csv**: The testing dataset, containing phrases to be classified into intents. |
|
|
| **Data Type:** Text data (phrases) with categorical labels (intents) |
|
|
| **Size:** |
|
|
| * **intents_train.csv**: [Insert number of rows/samples] phrases |
| * **intents_test.csv**: [Insert number of rows/samples] phrases |
|
|
| **Variables:** |
|
|
| * **Phrase**: The text input from users, representing their queries or requests. |
| * **Intent**: The categorical label assigned to each phrase, indicating the underlying goal or action. |
|
|
| **Data Collection:** The dataset was likely created by collecting phrases from various sources, such as customer interactions, online reviews, or forums, and then labeling them with corresponding intents. |
|
|
| **Data Processing:** The phrases were likely preprocessed by tokenizing, removing stop words, and stemming/lemmatizing to prepare them for model training. |
|
|
| **Task:** The task is to develop a model that can classify new, unseen phrases into their corresponding intents, based on the patterns learned from the training data. |
|
|
| **Potential Applications:** |
|
|
| * Improving the chat-bot's ability to understand user requests and respond accurately. |
| * Enhancing the overall customer experience by providing more effective support and guidance. |
| * Identifying trends and insights from user interactions to inform business decisions. |
|
|
|
|
| --- |
| license: mit |
| --- |
