| | --- |
| | 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 |
| |
|
| |
|
