metadata
language:
- en
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': question
'1': request
splits:
- name: train
num_bytes: 5642
num_examples: 87
- name: test
num_bytes: 14193
num_examples: 176
download_size: 15980
dataset_size: 19835
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
This dataset contains manually labeled examples used for training and testing reddgr/rq-request-question-prompt-classifier, a fine-tuning of DistilBERT that classifies chatbot prompts as either 'request' or 'question.'
It is part of a project aimed at identifying metrics to quantitatively measure the conversational quality of text generated by large language models (LLMs) and, by extension, any other type of text extracted from a conversational context (customer service chats, social media posts...).
Relevant Jupyter notebooks and Python scripts that use this dataset and related datasets and models can be found in the following GitHub repository: reddgr/chatbot-response-scoring-scbn-rqtl
Labels:
- 0: Question
- 1: Request