Upload sharded deduplicated dataset

README.md

@@ -2,28 +2,50 @@
 
 ## Overview
 
-This dataset is a curated mixture of high-quality text data designed for large language model pre-training, inspired by the Qwen3 methodology.
+This dataset is a curated mixture of high-quality text data designed for large language model pre-training, inspired by the Qwen3 methodology. The dataset includes both training and validation splits.
 
 ## Dataset Statistics
 
-**Total Size:** 10.19 billion tokens
+**Total Size:** 10.58 billion tokens
+- **Training Split:** 10.01 billion tokens (94.6%)
+- **Validation Split:** 0.57 billion tokens (5.4%)
 
-### Data Sources
+### Data Sources (Combined)
 
-- **dclm_baseline**: 6.21B tokens (60.92%) - 4,973,695 documents
-- **mini_pile**: 1.43B tokens (14.04%) - 999,249 documents
-- **common_corpus**: 1.01B tokens (9.87%) - 246,160 documents
-- **the_stack**: 0.96B tokens (9.4%) - 248,650 documents
-- **math_pile**: 0.59B tokens (5.77%) - 66,729 documents
+- **dclm_baseline**: 6.36B tokens (60.13%) - 5,100,585 documents
+- **mini_pile**: 1.43B tokens (13.52%) - 999,518 documents
+- **common_corpus**: 1.03B tokens (9.71%) - 252,785 documents
+- **the_stack**: 0.97B tokens (9.2%) - 254,809 documents
+- **math_pile**: 0.79B tokens (7.43%) - 72,559 documents
+
+
+### Training Split Statistics
+
+- **dclm_baseline**: 6.04B tokens (60.33%) - 4,841,522 documents
+- **mini_pile**: 1.36B tokens (13.58%) - 949,537 documents
+- **common_corpus**: 0.94B tokens (9.4%) - 233,049 documents
+- **the_stack**: 0.88B tokens (8.83%) - 234,915 documents
+- **math_pile**: 0.79B tokens (7.85%) - 72,554 documents
+
+
+### Validation Split Statistics
+
+- **dclm_baseline**: 0.32B tokens (56.56%) - 259,063 documents
+- **the_stack**: 0.09B tokens (15.85%) - 19,894 documents
+- **common_corpus**: 0.09B tokens (15.1%) - 19,736 documents
+- **mini_pile**: 0.07B tokens (12.48%) - 49,981 documents
+- **math_pile**: 0.0B tokens (0.0%) - 5 documents
 
 
 ## Data Processing Pipeline
 
 1. **Data Collection**: Sourced from multiple high-quality datasets
-2. **Standardization**: All data transformed to consistent format with `text`, `info`, and `source_data` fields  
-3. **Exact Deduplication**: Removed identical documents
-4. **Near Deduplication**: Used MinHashLSH with Jaccard similarity threshold of 0.85
-5. **Quality Filtering**: Applied content-based filtering during processing
+2. **Standardization**: All data transformed to consistent format with `text`, `info`, and `source_data` fields
+3. **Train/Validation Split**: Created 95%/5% splits within each source dataset
+4. **Exact Deduplication**: Removed identical documents within each split
+5. **Near Deduplication**: Used MinHashLSH with Jaccard similarity threshold of 0.85
+6. **Quality Filtering**: Applied content-based filtering during processing
+7. **Shuffling**: Applied shuffling within each large shard for better data distribution
 
 ## Data Format
 
@@ -32,6 +54,12 @@ Each example contains:
 - `info`: Metadata from the original dataset (as string)
 - `source_data`: Source dataset identifier
 
+## Splits
+
+The dataset contains two splits:
+- `train`: Training data (95% of each source dataset)
+- `validation`: Validation data (5% of each source dataset)
+
 ## Tokenization
 
 Token counts were computed using the Llama3 tokenizer (`meta-llama/Meta-Llama-3-8B`).
@@ -41,7 +69,12 @@ Token counts were computed using the Llama3 tokenizer (`meta-llama/Meta-Llama-3-
 ```python
 from datasets import load_dataset
 
+# Load the entire dataset
 dataset = load_dataset("bluelightai-dev/qwen_clt_pretrain_data")
+
+# Load specific splits
+train_dataset = load_dataset("bluelightai-dev/qwen_clt_pretrain_data", split="train")
+val_dataset = load_dataset("bluelightai-dev/qwen_clt_pretrain_data", split="validation")
 ```
 
 ## Dataset Sources

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