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---
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: 9052
num_examples: 132
- name: test
num_bytes: 14391
num_examples: 182
download_size: 18297
dataset_size: 23443
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](https://huggingface.co/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](https://github.com/reddgr/chatbot-response-scoring-scbn-rqtl)
## Labels:
- **0**: Question
- **1**: Request
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