Datasets:

Ravert
/

test2

@@ -1,96 +1,39 @@
----
-size_categories: 1K<n<10K
-tags:
-- synthetic
-- datadesigner
-configs:
-- config_name: data
-  data_files: data/*.parquet
-  default: true
----
-<div style="display: flex; justify-content: space-between; align-items: flex-end; width: 100%; margin-bottom: 1rem;">
-<h1 style="flex: 1; margin: 0;">Test2</h1>
-<sub style="white-space: nowrap;">Made with ❤️ using 🦥 Unsloth Studio</sub>
-</div>
----
-ML_1 was generated with Unsloth Recipe Studio. It contains 1,000 generated records.
----
-## 🚀 Quick Start
-```python
-from datasets import load_dataset
-# Load the main dataset
-dataset = load_dataset("Ravert/test2", "data", split="train")
-df = dataset.to_pandas()
-```
----
-## 📊 Dataset Summary
-- **📈 Records**: 1,000
-- **📋 Columns**: 3
----
-## 📋 Schema & Statistics
-| Column | Type | Column Type | Unique (%) | Null (%) | Details |
-|--------|------|-------------|------------|----------|---------|
-| `llm_structured_1` | `dict` | llm-structured | 1000 (100.0%) | 0 (0.0%) | Tokens: 76 out / 394 in |
----
-## ⚙️ Generation Details
-Generated with 3 column configuration(s):
-- **llm-structured**: 1 column(s)
-- **seed-dataset**: 2 column(s)
-📄 Full configuration available in [`builder_config.json`](builder_config.json) and detailed metadata in [`metadata.json`](metadata.json).
----
-## 📚 Citation
-If you use Data Designer in your work, please cite the project as follows:
-```bibtex
-@misc{nemo-data-designer,
-  author = {The NeMo Data Designer Team, NVIDIA},
-  title = {NeMo Data Designer: A framework for generating synthetic data from scratch or based on your own seed data},
-  howpublished = {\url{https://github.com/NVIDIA-NeMo/DataDesigner}},
-  year = 2026,
-  note = {GitHub Repository},
-}
-```
----
-## 💡 About NeMo Data Designer
-NeMo Data Designer is a general framework for generating high-quality synthetic data that goes beyond simple LLM prompting. It provides:
-- **Diverse data generation** using statistical samplers, LLMs, or existing seed datasets
-- **Relationship control** between fields with dependency-aware generation
-- **Quality validation** with built-in Python, SQL, and custom local and remote validators
-- **LLM-as-a-judge** scoring for quality assessment
-- **Fast iteration** with preview mode before full-scale generation
-For more information, visit: [https://github.com/NVIDIA-NeMo/DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner) (`pip install data-designer`)

+---
+language:
+- pl
+tags:
+- unsloth
+- alpaca
+- machine-learning
+- python
+- exam-prep
+configs:
+- config_name: data
+  data_files: data/*.parquet
+  default: true
+---
+# Test2 - Exam Ready
+Poprawiona wersja datasetu pod kolokwium z uczenia maszynowego w Pythonie.
+Zamiast prostego QA z cytatem dataset uczy model:
+- rozpoznawać typ problemu po opisie datasetu,
+- wykonać EDA i preprocessing,
+- dobrać model albo metodę,
+- napisać kod Python,
+- wskazać metryki i typowe pułapki.
+## Format
+Alpaca / Unsloth:
+- `instruction`
+- `input`
+- `output`
+Metadata:
+- `topic`
+- `task_type`
+- `source_file`
+Records: 1000

builder_config.json CHANGED Viewed

@@ -1,100 +1,50 @@
-{
-  "data_designer": {
-    "columns": [
-      {
-        "name": "llm_structured_1",
-        "drop": false,
-        "allow_resize": false,
-        "column_type": "llm-structured",
-        "prompt": "Given ONLY this chunk: {{ chunk_text }} generate one answerable question, answer, and exact supporting quote from chunk. If not answerable, skip.",
-        "model_alias": "provider_column",
-        "system_prompt": null,
-        "multi_modal_context": null,
-        "tool_alias": null,
-        "with_trace": "none",
-        "extract_reasoning_content": false,
-        "output_format": {
-          "type": "object",
-          "additionalProperties": false,
-          "required": [
-            "question",
-            "answer",
-            "evidence_quote"
-          ],
-          "properties": {
-            "question": {
-              "type": "string"
-            },
-            "answer": {
-              "type": "string"
-            },
-            "evidence_quote": {
-              "type": "string"
-            }
-          }
-        }
-      },
-      {
-        "name": "chunk_text",
-        "drop": false,
-        "allow_resize": false,
-        "column_type": "seed-dataset"
-      },
-      {
-        "name": "source_file",
-        "drop": false,
-        "allow_resize": false,
-        "column_type": "seed-dataset"
-      }
-    ],
-    "model_configs": [
-      {
-        "alias": "provider_column",
-        "model": "deepseek/deepseek-v4-pro",
-        "inference_parameters": {
-          "generation_type": "chat-completion",
-          "max_parallel_requests": 4,
-          "timeout": null,
-          "extra_body": null,
-          "temperature": 0.7,
-          "top_p": null,
-          "max_tokens": null
-        },
-        "provider": "provider_1",
-        "skip_health_check": false
-      }
-    ],
-    "tool_configs": [],
-    "seed_config": {
-      "source": {
-        "seed_type": "unstructured",
-        "paths": [
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/b8a4fa2c175f457db0feb8273f9075d7.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/bec20ec612e44e85aea6de58d27f7501.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/c736eb5e65824b6fba5e88de4b471c62.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/b71db9b5c01f43e196389fa4462183ce.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/318bd502c87b4e688a2f6af51641d094.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/2537bdf9c2aa4797a3a266b00d2ee78a.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/8591cd495c2943c1aa6f2bb89a2425b6.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/44221de339164189badcdd6aa2994f0b.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/ce4f2cad5f1a4a8ba05f96f2561be512.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/67a2008bde0847e1bba17fc7939a2c50.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/18c486022fa64a8b88a5fe09a61fab8f.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/a92a61391233433da2dec0a04247e630.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/627d83e612894740885cd5c6d489f154.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/b9d52a3d1007410c8b575d70dd8779e8.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/b1a8c5a4dda54b3f935d5d2537e2a88c.extracted.txt",
-          "/root/.unsloth/studio/assets/datasets/unstructured-uploads/n4/bb0c143f913f4c58a32872c024301c8b.extracted.txt"
-        ],
-        "chunk_size": 1200,
-        "chunk_overlap": 200
-      },
-      "sampling_strategy": "ordered",
-      "selection_strategy": null
-    },
-    "constraints": null,
-    "profilers": null,
-    "processors": null
-  },
-  "library_version": "0.5.4"
 }

+{
+  "data_designer": {
+    "columns": [
+      {
+        "name": "instruction",
+        "drop": false,
+        "allow_resize": false,
+        "column_type": "seed-dataset"
+      },
+      {
+        "name": "input",
+        "drop": false,
+        "allow_resize": false,
+        "column_type": "seed-dataset"
+      },
+      {
+        "name": "output",
+        "drop": false,
+        "allow_resize": false,
+        "column_type": "seed-dataset"
+      },
+      {
+        "name": "topic",
+        "drop": false,
+        "allow_resize": false,
+        "column_type": "seed-dataset"
+      },
+      {
+        "name": "task_type",
+        "drop": false,
+        "allow_resize": false,
+        "column_type": "seed-dataset"
+      },
+      {
+        "name": "source_file",
+        "drop": false,
+        "allow_resize": false,
+        "column_type": "seed-dataset"
+      }
+    ],
+    "seed_config": {
+      "source": {
+        "seed_type": "curated-exam-ready"
+      },
+      "sampling_strategy": "balanced-by-lab-topic"
+    }
+  },
+  "library_version": "0.5.4-compatible",
+  "note": "Rebuilt for Unsloth Alpaca-style fine-tuning for ML/Python exam preparation."
 }

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metadata.json CHANGED Viewed

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-{
-  "actual_num_records": 1000,
-  "buffer_size": 1000,
-  "column_statistics": [
-    {
-      "column_name": "llm_structured_1",
-      "column_type": "llm-structured",
-      "input_tokens_mean": 394.2,
-      "input_tokens_median": 389.0,
-      "input_tokens_stddev": 49.86,
-      "num_null": 0,
-      "num_records": 1000,
-      "num_unique": 1000,
-      "output_tokens_mean": 75.57,
-      "output_tokens_median": 70.0,
-      "output_tokens_stddev": 30.75,
-      "pyarrow_dtype": "struct<answer: string, evidence_quote: string, question: string>",
-      "simple_dtype": "dict"
-    }
-  ],
-  "dataset_name": "recipe_ml-1",
-  "file_paths": {
-    "data": [
-      "data/batch_00000.parquet"
-    ]
-  },
-  "num_completed_batches": 1,
-  "schema": {
-    "chunk_text": "string",
-    "llm_structured_1": "struct<answer: string, evidence_quote: string, question: string>",
-    "source_file": "string"
-  },
-  "target_num_records": 1000,
-  "total_num_batches": 1
 }

+{
+  "records": 1000,
+  "schema": {
+    "instruction": "string",
+    "input": "string",
+    "output": "string",
+    "topic": "string",
+    "task_type": "string",
+    "source_file": "string"
+  },
+  "topic_counts": {
+    "braki danych i outliery": 85,
+    "podział danych i walidacja": 89,
+    "normalizacja i kodowanie": 87,
+    "regresja logistyczna": 91,
+    "KNN": 80,
+    "PCA": 87,
+    "regresja liniowa": 78,
+    "klasyfikacja": 78,
+    "wizualizacja danych": 86,
+    "drzewa decyzyjne": 90,
+    "klasteryzacja": 75,
+    "pozyskiwanie danych": 74
+  },
+  "task_type_counts": {
+    "code_repair": 30,
+    "lecture_concept_application": 40,
+    "md_exercise_solution": 124,
+    "exam_scenario": 50,
+    "exam_dataset_scenario": 756
+  },
+  "goal": "Fine-tuning pod kolokwium: student dostaje dataset i stosuje wiedzę z labów 1-12."
 }