Update README.md

README.md

@@ -5,4 +5,34 @@ language:
 - en
 size_categories:
 - 100B<n<1T
----
+---
+
+This dataset contains the fully prepared data (tokenised and pre-shuffled) used to train the Pythia (deduped) models—which you can find under the EleutherAI organisation and also listed in my [Memerisation-Profiles collection](https://huggingface.co/collections/pietrolesci/memorisation-profiles-6619604c4594c878cd9d451f).
+It is the very same data as [EleutherAI/pile-deduped-pythia-preshuffled](https://huggingface.co/datasets/EleutherAI/pile-deduped-pythia-preshuffled) in a more manageable format. 
+That is, instead of storing the data in the Megatron format used by the GPT-NeoX library, I stored them in a parquet format.
+
+
+## Format
+
+The dataset has 3 columns:
+- `uid`: a sequential identified for the sequence (not present in the original dataset)
+- `batch_idx`: the index of the batch to which a sequence belongs to (not present in the original dataset).
+- `token_ids`: the tokenised texts, each of length 2049 tokens.
+
+The dataset is split into 143 chunks (i.e., parquet files), each corresponding to the data seen by a checkpoint.
+The Pythia model checkpoints are available at initialisation (i.e., step 0) and each 1000 steps (i.e., steps 1000, 2000, etc) up to the last checkpoint (i.e., step 143000).
+We reflect this into the filenames: `train-001000.parquet`, `train-002000.parquet`, ..., `train-143000.parquet`.
+
+**Example**: consider file `train-001000.parquet`. It contains sequences with `batch_idx` 0-999. These sequences where "seen" (i.e., the model took a gradient step on them) before taking checkpoint 1000.
+
+Note that additional log-spaced checkpoints are available for the initial part of training (i.e., steps 1, 2, 4, 8, ..., 512). I did not create a separate file for these, but you can easily subset the first chunk (i.e., `data/train-001000.parquet`).
+
+
+## License
+
+For the license, refer to the original dataset ([EleutherAI/pile-deduped-pythia-preshuffled](https://huggingface.co/datasets/EleutherAI/pile-deduped-pythia-preshuffled)).
+
+
+## Acknowledgements
+
+Kudos to [LLM360/AmberDatasets](https://huggingface.co/datasets/LLM360/AmberDatasets), which inspired this release.