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README.md

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-license: cc-by-sa-3.0
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+---
+license: cc-by-sa-3.0
+pretty_name: dolly 15k enriched
+---
+Databricks - Dolly 15k – Enriched Variant (Instruction-Tuned with Semantic and Complexity Augmentation)
+Overview
+This dataset is a semantically enriched and complexity-aware extension of the original Databricks Dolly 15k, purpose-built for evaluating and training instruction-following models. Each sample is augmented with additional signals to enable more nuanced filtering, curriculum learning, and benchmark development across diverse NLP tasks.
+
+Dataset Format
+Each sample includes the following fields:
+
+instruction (str) – the prompt or task instruction
+
+context (str) – optional background text (can be empty)
+
+response (str) – the ideal output corresponding to the instruction
+
+category (str) – the original category label (e.g., closed_qa, generation)
+
+category_enriched (List[str]) – multi-class enriched taxonomy based on LLM-based relabeling
+
+embedding (List[float]) – 384-dimensional semantic representation using a SentenceTransformer model
+
+instruction_readability (float) – Flesch Reading Ease score of the instruction
+
+response_readability (float) – Flesch Reading Ease score of the response
+
+instruction_tokens (int) – number of tokens in the instruction
+
+response_tokens (int) – number of tokens in the response
+
+Enrichment Details
+1. Semantic Embeddings (384-D)
+Model: all-MiniLM-L6-v2 from SentenceTransformers
+
+Purpose:
+
+Enables vector search and similarity-based retrieval.
+
+Facilitates clustering or curriculum grouping based on semantic distance.
+
+Use Case: RAG pipelines, hybrid retriever-generator evaluation, semantic data deduplication.
+
+2. Multi-Label Category Enrichment
+Method: LLM-based enrichment of original category into multiple labels reflecting nuanced intent (e.g., closed_qa, classification, instruction_reformulation).
+
+Purpose:
+
+Allows for filtering and training multi-task models with fine-grained task type supervision.
+
+Enables few-shot sampling or balanced evaluation subsets.
+
+Use Case: Model generalization studies, task disambiguation training, LLM taxonomy alignment.
+
+3. Readability Scores
+Metric: Flesch Reading Ease
+
+Range: Typically from -10 (very complex/short text) to 100+ (very easy to read)
+
+Interpretation:
+
+Higher is simpler: A score above 60 indicates easy-to-read content.
+
+Negative values: Usually from one-word answers or very short instructions.
+
+Purpose:
+
+Measures linguistic complexity for curriculum learning.
+
+Enables filtering of prompts based on difficulty level.
+
+4. Token Lengths (Instruction/Response)
+Method: tiktoken tokenizer for gpt-3.5-turbo vocabulary
+
+Purpose:
+
+Supports token-level curriculum filtering.
+
+Enables outlier detection for unusually long or short samples.
+
+Use Case: Model length conditioning, latency profiling, instruction tuning length analysis.
+
+Research Use Cases
+Curriculum Learning: Use readability and token length to gradually train models from simple to complex examples.
+
+Semantic Similarity Evaluation: Leverage embeddings for nearest-neighbor search, duplicate detection, or hybrid retriever training.
+
+Task-Type Robustness: Train and evaluate models across enriched multi-label categories to assess generalization across QA, classification, and generative tasks.
+
+Prompt Engineering Validation: Analyze impact of prompt complexity (via readability/tokens) on response quality.
+
+Citation (Original)
+bibtex
+Copy
+Edit
+@misc{databricks2023dolly,
+  author = {Databricks},
+  title = {Databricks Dolly 15k: Instruction-Tuned LLM Dataset},
+  year = {2023},
+  url = {https://huggingface.co/datasets/databricks/databricks-dolly-15k}
+}
+License
+Same as original Dolly 15k: Creative Commons Attribution-ShareAlike 3.0 (CC BY-SA 3.0)