Update README.md

README.md

@@ -4,92 +4,177 @@ language:
 - en
 source_datasets:
 - HuggingFaceFW/fineweb
-dataset_info:
-  features:
-  - name: token_ids
-    dtype: uint16
-  splits:
-  - name: shard
-    num_bytes: 3628
-    num_examples: 1814
-  download_size: 6346
-  dataset_size: 3628
 configs:
 - config_name: default
   data_files:
   - split: shard
-    path: data/shard-*
+    path:
+      - data_1/shard-*
+      - data_2/shard-*
+      - data_3/shard-*
+      - data_4/shard-*
+      - data_5/shard-*
+      - data_6/shard-*
+      - data_7/shard-*
+      - data_8/shard-*
+      - data_9/shard-*
+      - data_10/shard-*
+      - data_11/shard-*
+      - data_12/shard-*
+      - data_13/shard-*
+      - data_14/shard-*
+      - data_15/shard-*
+      - data_16/shard-*
+      - data_17/shard-*
+      - data_18/shard-*
+      - data_19/shard-*
+      - data_20/shard-*
 ---
 
 # FineWeb Tokenized Corpus (AnisoleAI)
 
 ## Overview
 
-This repository provides:
+This repository provides a large-scale **pre-tokenized version of the FineWeb dataset** designed for efficient training of language models.
 
-- A pretrained SentencePiece tokenizer (`tokenizer.model`) trained on a subset of FineWeb.
-- A pre-tokenized corpus derived from FineWeb for efficient LLM/SLM pretraining.
+The dataset contains text from the **FineWeb corpus** that has been tokenized using a **SentencePiece tokenizer**.  
+Tokens are stored in a compact **`uint16` format** for efficient storage and high-throughput training.
 
-No semantic modifications were made to the original dataset. The corpus was tokenized and stored in a memory-efficient `uint16` format for direct use in training pipelines.
+Each dataset record contains a **flat array of token IDs** representing a continuous tokenized text sequence.
+
+This format allows:
+
+- fast loading
+- minimal memory overhead
+- efficient distributed training
+- direct compatibility with LLM training pipelines
+
+No semantic modifications were made to the original FineWeb dataset.  
+The text was only **tokenized and serialized into shard files**.
+
+---
+
+# Dataset Structure
+
+The dataset is organized into multiple shard directories:
+
+```
+data_1/shard-00000.parquet
+data_1/shard-00001.parquet
+...
+
+data_2/shard-00000.parquet
+...
+
+...
+
+data_20/shard-XXXXX.parquet
+```
+
+Each shard contains:
+
+```
+token_ids: uint16[]
+```
+
+Each record stores a contiguous tokenized segment that can be used directly for model training.
 
 ---
 
-## 🚀 Usage
+# Loading the Dataset
 
-### 1. Load the Tokenized Data
-You can stream or download the shards using the Hugging Face `datasets` library.
+You can load the dataset using the HuggingFace `datasets` library.
 
 ```python
 from datasets import load_dataset
-import numpy as np
 
-# Load the "shard" split
-ds = load_dataset("anisoleai/fineweb-tokenized", split="shard")
+dataset = load_dataset(
+    "anisoleai/fineweb-tokenized",
+    split="shard"
+)
+
+sample = dataset[0]["token_ids"]
 
-# Access tokens from the first record
-# Each record contains a flat array of token_ids
-sample_tokens = ds[0]["token_ids"]
-print(f"Loaded {len(sample_tokens):,} tokens.")
+print("Number of tokens:", len(sample))
 ```
 
-### 2. Load the Tokenizer
-The `tokenizer.model` file is included in this repository.
+The dataset supports:
+
+- streaming
+- distributed loading
+- partial downloads
+
+---
+
+# Loading the Tokenizer
+
+The tokenizer used to generate the corpus is included in this repository.
 
 ```python
 import sentencepiece as spm
 from huggingface_hub import hf_hub_download
 
-# Download the model file from the dataset repo
 model_path = hf_hub_download(
-    repo_id="anisoleai/fineweb-tokenized", 
-    filename="tokenizer.model", 
+    repo_id="anisoleai/fineweb-tokenized",
+    filename="tokenizer.model",
     repo_type="dataset"
 )
 
-# Initialize SentencePiece
 sp = spm.SentencePieceProcessor(model_file=model_path)
-print(f"Vocab size: {sp.get_piece_size()}")
 
-# Example decoding
+print("Vocabulary size:", sp.get_piece_size())
+
 print(sp.decode([1, 10, 20, 30]))
 ```
 
 ---
 
-## 📄 Source & License
+# Intended Use
+
+This dataset is intended for:
+
+- large language model pretraining
+- tokenizer benchmarking
+- distributed LLM training pipelines
+- academic AI research
+- commercial AI development
+
+The shard-based structure allows scalable multi-worker training pipelines.
+
+---
+
+# Source Dataset
+
+Original dataset:
+
+**FineWeb**  
+https://huggingface.co/datasets/HuggingFaceFW/fineweb
+
+FineWeb is a large-scale filtered web corpus designed for training language models.
+
+---
+
+# License
+
+This dataset follows the license of the original dataset:
+
+**Open Data Commons Attribution License (ODC-BY) v1.0**
+
+https://opendatacommons.org/licenses/by/1-0/
+
+---
 
-**Original Dataset:** [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) (ODC-BY 1.0)
+# Attribution
 
-This tokenized corpus is distributed under the **Open Data Commons Attribution License (ODC-BY) v1.0**.
+If you use this dataset, please attribute:
 
-**Attribution Requirements:**
-- Attribute the FineWeb dataset creators.
-- Attribute **AnisoleAI** for the tokenization processing.
+- the creators of the FineWeb dataset
+- **AnisoleAI** for the tokenization pipeline and dataset preparation
 
 ---
 
-## 🎯 Intended Use
+# Notes
 
-- LLM / SLM pretraining
-- Tokenizer benchmarking
-- Research and commercial AI development
+- The dataset contains **token IDs only**.
+- Original raw text is **not included**.
+- Token IDs correspond to the included **SentencePiece tokenizer**.