---
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
---