Update README.md

README.md

@@ -1,4 +1,6 @@
 ---
+language:
+- en
 dataset_info:
   features:
   - name: text
@@ -21,4 +23,29 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- llms
+- nlp
+- chatbots
+- prompts
+pretty_name: TL (Test vs Learn) chatbot prompts
+size_categories:
+- n<1K
 ---
+
+This dataset contains manually labeled examples used for training and testing [reddgr/tl-test-learn-prompt-classifier](https://huggingface.co/reddgr/tl-test-learn-prompt-classifier), a fine-tuning of DistilBERT that classifies chatbot prompts as either 'test' or 'learn.'
+
+Prompts labeled as 'test' (1) are those where it can be inferred that the user is intentionally 'challenging' the conversational tool with a complicated question the user might know the answer to, or a subjective question the user makes with the purpose of testing the tool rather than learning from it or obtaining a specific unknown information.
+
+An alternative naming convention for the labels is 'problem' (test) vs 'instruction' (learn). The earliest versions of the reddgr/tl-test-learn-prompt-classifier model used a zero-shot classification pipeline for those two specific terms: instruction (0) vs problem (1).
+
+This dataset and the model are 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**: Learn (instruction)
+- **1**: Test (problem)