Upload README.md with huggingface_hub

README.md

@@ -3,41 +3,133 @@ language:
 - en
 task_categories:
 - text-generation
+- conversational
+- instruction-following
 size_categories:
 - n<1M
+tags:
+- youtube
+- transcripts
+- llm-training
+- fine-tuning
+- whisper
+- conversational-ai
 ---
 
-# YouTube Transcripts Dataset
+# YouTube Transcripts Dataset for LLM Training
 
-This dataset contains transcripts from YouTube videos, collected for LLM training purposes.
+This dataset contains high-quality, structured transcripts from YouTube videos, specifically formatted for Large Language Model (LLM) training and fine-tuning.
 
 ## Dataset Structure
 
-The dataset is stored in JSONL format with automatic sharding (max 10MB per shard).
+The dataset is optimized for LLM training with the following structure:
 
-Each entry contains:
+### Core Training Fields
+- `text`: Cleaned and normalized transcript text
+- `instruction`: Instruction format for fine-tuning (e.g., "Provide a transcript of the video titled '...'")
+- `response`: The transcript content (same as `text` but in instruction-response format)
+
+### Content Analysis
+- `word_count`: Number of words in the transcript
+- `character_count`: Number of characters
+- `estimated_tokens`: Estimated token count for training
+- `quality_score`: Quality score (0-1) based on length, structure, and metadata
+- `content_type`: Classified content type (educational, conversational, instructional, narrative, general)
+
+### Metadata
 - `video_id`: YouTube video ID
-- `text`: Transcript text
-- `method`: Transcription method (youtube or whisper)
-- `timestamp`: When the transcript was added
-- `metadata`: Additional metadata
+- `source`: Always "youtube"
+- `transcription_method`: "whisper" (OpenAI Whisper)
+- `language`: "en" (English)
+- `timestamp`: Processing timestamp
+- `video_metadata`: Structured video information
+  - `title`: Video title
+  - `channel`: Channel name
+  - `duration_seconds`: Video duration in seconds
+  - `duration_formatted`: Human-readable duration (MM:SS or HH:MM:SS)
+  - `upload_date`: Video upload date
+  - `view_count`: Number of views
+  - `category`: Auto-classified category (education, business, health, technology, etc.)
 
 ## Loading the Dataset
 
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("morka17/rtu-tgn")
+# Load the complete dataset
+dataset = load_dataset("morka17/rtu-tgn", data_files="data_shard_*.jsonl")
+
+# For instruction fine-tuning
+train_data = dataset['train']
+for example in train_data:
+    instruction = example['instruction']
+    response = example['response']
+    # Use for instruction-following fine-tuning
+
+# For general language modeling
+for example in train_data:
+    text = example['text']
+    # Use for general language model training
+```
+
+## Filtering and Quality Control
+
+```python
+# Filter by quality score
+high_quality = dataset.filter(lambda x: x['quality_score'] > 0.7)
+
+# Filter by content type
+educational_content = dataset.filter(lambda x: x['content_type'] == 'educational')
+
+# Filter by length (optimal for training)
+optimal_length = dataset.filter(lambda x: 1000 <= x['word_count'] <= 5000)
+
+# Filter by category
+business_content = dataset.filter(lambda x: x['video_metadata']['category'] == 'business')
 ```
 
-## Shards
+## Use Cases
+
+### 1. **Instruction Fine-tuning**
+Use the `instruction` and `response` fields for training models to follow instructions.
 
-The dataset is split into multiple shard files (data_shard_XXXX.jsonl) for easier handling.
+### 2. **Conversational AI Training**
+Filter for `content_type == 'conversational'` for dialogue training.
+
+### 3. **Domain-specific Training**
+Filter by `video_metadata.category` for domain-specific fine-tuning.
+
+### 4. **Quality-based Training**
+Use `quality_score` to select high-quality training examples.
+
+## Data Quality
+
+- **Text Cleaning**: Transcripts are cleaned to remove artifacts, normalize punctuation, and improve readability
+- **Quality Scoring**: Each entry has a quality score based on length, structure, punctuation, and metadata
+- **Content Classification**: Automatic classification into content types for targeted training
+- **Metadata Enrichment**: Rich metadata for filtering and analysis
+
+## Sharding
+
+The dataset is automatically sharded into files of max 10MB each (data_shard_XXXX.jsonl) for efficient loading and processing.
 
 ## Last Updated
 
-2025-10-03T11:41:26.041305
+2025-10-06T21:55:58.000864
 
-## License
+## License and Usage
 
-Please ensure compliance with YouTube's Terms of Service when using this dataset.
+Please ensure compliance with YouTube's Terms of Service when using this dataset. This dataset is intended for research and educational purposes in natural language processing and machine learning.
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```
+@dataset{youtube_transcripts_llm,
+  title={YouTube Transcripts Dataset for LLM Training},
+  author={Generated via OpenAI Whisper},
+  year={2025},
+  url={https://huggingface.co/datasets/morka17/rtu-tgn}
+}
+```