Update README.md

README.md

@@ -1,60 +1,67 @@
----
-license: apache-2.0
-task_categories:
-- text-generation
-language:
-- en
-tags:
-- falcon
-- refinedweb
-- 1B
-- parquet
-size_categories:
-- 1B<n<10B
----
-
-# Falcon RefinedWeb 1B
-
-## Dataset Description
-This is a **1.01 Billion token** subset of the [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) dataset. It was created by streaming the dataset with a large shuffle buffer to ensure a random, representative sample of the web data.
-
-## Motivation
-RefinedWeb is a high-quality filtered web dataset, but the full version is massive. This 1B token slice provides a perfect testbed for evaluating model architecture changes or for use in curriculum learning experiments.
-
-## Dataset Details
-- **Total Tokens:** 1,005,000,041 (~1.01B)
-- **Source:** [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
-- **Method:** Streamed with shuffle buffer (size=5000).
-- **Format:** Parquet (Snappy compression)
-- **Producer:** Kris Bailey (kris@krisbailey.com)
-
-## Usage
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("krisbailey/falcon-refinedweb-1B", split="train")
-print(ds[0])
-```
-
-## Subsets & Slicing
-Since this dataset was randomly shuffled during creation, you can safely slice it to get smaller, representative datasets (e.g., for scaling laws experiments) without needing to download the full dataset.
-
-```python
-# 100M Token Subset (approx 10%)
-ds_100m = load_dataset("krisbailey/falcon-refinedweb-1B", split="train[:10%]")
-
-# 500M Token Subset (approx 50%)
-ds_500m = load_dataset("krisbailey/falcon-refinedweb-1B", split="train[:50%]")
-```
-
-## Citation
-```bibtex
-@article{penedo2023refinedweb,
-  title={The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only},
-  author={Penedo, Guilherme and Malartic, Quentin and Hesslow, Daniel and Cojocaru, Ruxandra and Cappelli, Alessandro and Alobeidli, Hamza and Pannier, Baptiste and Almazrouei, Ebtesam and Launay, Julien},
-  journal={arXiv preprint arXiv:2306.01116},
-  year={2023}
-}
-```
-
+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- falcon
+- refinedweb
+- 1B
+- parquet
+- web-refined
+- text-generation
+- clean-web-corpus
+- llm-pretrain
+- domain-agnostic
+- sentence-quality-filtered
+- huggingface-refinedweb
+size_categories:
+- 1B<n<10B
+---
+
+# Falcon RefinedWeb 1B
+
+## Dataset Description
+This is a **1.01 Billion token** subset of the [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) dataset. It was created by streaming the dataset with a large shuffle buffer to ensure a random, representative sample of the web data.
+
+## Motivation
+RefinedWeb is a high-quality filtered web dataset, but the full version is massive. This 1B token slice provides a perfect testbed for evaluating model architecture changes or for use in curriculum learning experiments.
+
+## Dataset Details
+- **Total Tokens:** 1,005,000,041 (~1.01B)
+- **Source:** [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+- **Method:** Streamed with shuffle buffer (size=5000).
+- **Format:** Parquet (Snappy compression)
+- **Producer:** Kris Bailey (kris@krisbailey.com)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("krisbailey/falcon-refinedweb-1B", split="train")
+print(ds[0])
+```
+
+## Subsets & Slicing
+Since this dataset was randomly shuffled during creation, you can safely slice it to get smaller, representative datasets (e.g., for scaling laws experiments) without needing to download the full dataset.
+
+```python
+# 100M Token Subset (approx 10%)
+ds_100m = load_dataset("krisbailey/falcon-refinedweb-1B", split="train[:10%]")
+
+# 500M Token Subset (approx 50%)
+ds_500m = load_dataset("krisbailey/falcon-refinedweb-1B", split="train[:50%]")
+```
+
+## Citation
+```bibtex
+@article{penedo2023refinedweb,
+  title={The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only},
+  author={Penedo, Guilherme and Malartic, Quentin and Hesslow, Daniel and Cojocaru, Ruxandra and Cappelli, Alessandro and Alobeidli, Hamza and Pannier, Baptiste and Almazrouei, Ebtesam and Launay, Julien},
+  journal={arXiv preprint arXiv:2306.01116},
+  year={2023}
+}
+```
+