Update README.md

README.md

@@ -1,50 +1,56 @@
----
-license: odc-by
-task_categories:
-- text-generation
-language:
-- en
-tags:
-- rp
-- 100M
-- parquet
-size_categories:
-- 100M<n<1B
----
-
-# RedPajama-Data-V2-100M
-
-## Dataset Description
-This is a **100.0 Million token** subset of [krisbailey/RedPajama-Data-V2-1B](https://huggingface.co/datasets/krisbailey/RedPajama-Data-V2-1B), which is a subset of [togethercomputer/RedPajama-Data-V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2).
-
-## Motivation
-100M tokens is a standard size for:
-- **CI/CD Pipelines:** Fast enough to download and train for unit tests.
-- **Debugging:** Verifying training loops without waiting for hours.
-- **Scaling Laws:** The first step in a logarithmic scaling series (100M -> 1B -> 10B).
-
-## Dataset Details
-- **Total Tokens:** 99,999,721
-- **Source:** krisbailey/RedPajama-Data-V2-1B
-- **Structure:** First ~10% of the randomized 1B dataset.
-- **Format:** Parquet (Snappy compression) - Single File
-- **Producer:** Kris Bailey (kris@krisbailey.com)
-
-## Usage
-
-```python
-from datasets import load_dataset
-
-ds = load_dataset("krisbailey/RedPajama-Data-V2-100M", split="train")
-print(ds[0])
-```
-
-## Citation
-```bibtex
-@article{together2023redpajama,
-  title={RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
-  author={Together Computer},
-  journal={https://github.com/togethercomputer/RedPajama-Data},
-  year={2023}
-}
-```
+---
+license: odc-by
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- rp
+- 100M
+- parquet
+- redpajama
+- reference-reproduction
+- benchmark-subset
+- open-pretraining-data
+- reproducible-dataset
+- data-slicing
+size_categories:
+- 100M<n<1B
+---
+
+# RedPajama-Data-V2-100M
+
+## Dataset Description
+This is a **100.0 Million token** subset of [krisbailey/RedPajama-Data-V2-1B](https://huggingface.co/datasets/krisbailey/RedPajama-Data-V2-1B), which is a subset of [togethercomputer/RedPajama-Data-V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2).
+
+## Motivation
+100M tokens is a standard size for:
+- **CI/CD Pipelines:** Fast enough to download and train for unit tests.
+- **Debugging:** Verifying training loops without waiting for hours.
+- **Scaling Laws:** The first step in a logarithmic scaling series (100M -> 1B -> 10B).
+
+## Dataset Details
+- **Total Tokens:** 99,999,721
+- **Source:** krisbailey/RedPajama-Data-V2-1B
+- **Structure:** First ~10% of the randomized 1B dataset.
+- **Format:** Parquet (Snappy compression) - Single File
+- **Producer:** Kris Bailey (kris@krisbailey.com)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("krisbailey/RedPajama-Data-V2-100M", split="train")
+print(ds[0])
+```
+
+## Citation
+```bibtex
+@article{together2023redpajama,
+  title={RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
+  author={Together Computer},
+  journal={https://github.com/togethercomputer/RedPajama-Data},
+  year={2023}
+}
+```