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---
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
--- |