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--- |
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language: |
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- en |
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license: apache-2.0 |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': question |
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'1': request |
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splits: |
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- name: train |
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num_bytes: 9052 |
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num_examples: 132 |
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- name: test |
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num_bytes: 14391 |
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num_examples: 182 |
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download_size: 18297 |
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dataset_size: 23443 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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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.' |
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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...). |
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Relevant Jupyter notebooks and Python scripts that use this dataset and related datasets and models can be found in the following GitHub repository: |
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[reddgr/chatbot-response-scoring-scbn-rqtl](https://github.com/reddgr/chatbot-response-scoring-scbn-rqtl) |
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## Labels: |
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- **0**: Question |
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- **1**: Request |
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