Datasets:

GenAIDevTOProd
/

databricks-dolly15k-semantic-complexity

@@ -1,12 +1,28 @@
 ---
 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
@@ -29,8 +45,10 @@ 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:
@@ -41,7 +59,8 @@ 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:
@@ -52,7 +71,8 @@ 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)
@@ -69,7 +89,8 @@ 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:
@@ -80,7 +101,8 @@ 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.
@@ -89,15 +111,16 @@ Task-Type Robustness: Train and evaluate models across enriched multi-label cate
 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)

 ---
 license: cc-by-sa-3.0
 pretty_name: dolly 15k enriched
+language:
+- en
+tags:
+- databricks
+- dolly
+- NLP
+- semantic
+- llm-evaluation
+- fine-tuning
+- text-statistics
+- sentence-transformers
+- embedding
+- faiss-compatible
 ---
+## 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
 response_tokens (int) – number of tokens in the response
+## Enrichment Details
+# 1. Semantic Embeddings (384-D)
 Model: all-MiniLM-L6-v2 from SentenceTransformers
 Purpose:
 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:
 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)
 Enables filtering of prompts based on difficulty level.
+# 4. Token Lengths (Instruction/Response)
 Method: tiktoken tokenizer for gpt-3.5-turbo vocabulary
 Purpose:
 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.
 Prompt Engineering Validation: Analyze impact of prompt complexity (via readability/tokens) on response quality.
+## Citation (Original)
+@online{DatabricksBlog2023DollyV2,
+    author    = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
+    title     = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
+    year      = {2023},
+    url       = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
+    urldate   = {2023-06-30}
 }
+## License
 Same as original Dolly 15k: Creative Commons Attribution-ShareAlike 3.0 (CC BY-SA 3.0)