Update README.md

README.md

@@ -1,3 +1,67 @@
----
-license: mit
----
+---
+license: mit
+---
+# Stage 1 Packed Pretraining Dataset
+
+This dataset contains preprocessed and token-packed `.bin` files intended for use in pretraining a decoder-only Transformer language model.
+
+## Dataset Contents
+
+- Each `.bin` file contains a fixed number of samples, where each sample is exactly 8192 tokens long.
+- Samples are grouped into batches of 125 samples, totaling **1.024 million tokens per batch**.
+- Each file (called a "block") contains 12500 samples (approximately 102.4 million tokens).
+- All samples are tokenized using the `GPT2TokenizerFast` from Hugging Face Transformers.
+
+## Structure
+
+- Format: Binary files (`int32`) containing token IDs.
+- File naming: `stage1_block_0000.bin`, `stage1_block_0001.bin`, etc.
+- Tokenizer: `GPT2TokenizerFast` with `eos_token` used as a separator and padding token.
+- Context length: 8192 tokens per sample.
+
+## Source Datasets
+
+Tokens were drawn from a diverse mix of high-quality open datasets:
+
+- `C4 (en)`
+- `Wikipedia (2023/11 dump)`
+- `OpenWebText`
+- `CCNews`
+- `Gutenberg`
+- `arXiv`
+- `BookCorpus Open`
+- `S2ORC`
+- `TriviaQA`
+- `PAQ`
+- `Natural Questions`
+
+Each dataset was assigned a token quota (e.g., 10B tokens) to ensure a balanced mix.
+
+## Preprocessing & Packing Strategy
+
+- Samples were **streamed** using Hugging Face Datasets with shuffling.
+- Texts were **tokenized**, filtered using a garbage filter, and concatenated with separator tokens.
+- Samples were packed into fixed-length chunks of 8192 tokens.
+- Leftover tokens from one batch are carried forward to the next to ensure no token duplication or loss.
+
+### Garbage Filtering Heuristics:
+- Removed texts with:
+  - Too few words or characters.
+  - High symbol-to-alphanumeric ratio.
+  - Excessive character repetition.
+  - Very low word diversity.
+
+## Usage Example
+
+You can load and decode tokens using PyTorch:
+
+```python
+import torch
+from transformers import GPT2TokenizerFast
+
+tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
+with open("stage1_block_0000.bin", "rb") as f:
+    tokens = torch.frombuffer(f.read(), dtype=torch.int32)
+    sample = tokens[:8192].tolist()
+    text = tokenizer.decode(sample)
+    print(text)