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.