Update README.md

README.md

@@ -7,9 +7,11 @@ size_categories:
 - 100B<n<1T
 ---
 
-This dataset contains the fully prepared data, which has been tokenized and pre-shuffled, used to train the Pythia (deduplicated) models. You can find these models under the EleutherAI organization, and they are also listed in my [Memorisation Profiles collection](https://huggingface.co/collections/pietrolesci/memorisation-profiles-6619604c4594c878cd9d451f).
+This dataset contains the fully prepared data, which has been tokenized and pre-shuffled, used to train the Pythia (deduplicated) models. 
+You can find these models under the EleutherAI organisation, and they are also listed in my [Memorisation-Profiles collection](https://huggingface.co/collections/pietrolesci/memorisation-profiles-6619604c4594c878cd9d451f).
 
-This data is the same as the one found in [EleutherAI/pile-deduped-pythia-preshuffled](https://huggingface.co/datasets/EleutherAI/pile-deduped-pythia-preshuffled), but it is presented in a more manageable format. Instead of using the Megatron format used by the GPT-NeoX library, I have stored the data in a parquet format.
+This data is the same as the one found in [EleutherAI/pile-deduped-pythia-preshuffled](https://huggingface.co/datasets/EleutherAI/pile-deduped-pythia-preshuffled), 
+but it is presented in a more manageable format. Instead of using the Megatron format used by the GPT-NeoX library, I have stored the data in a parquet format.
 
 
 ## Format
@@ -29,7 +31,8 @@ Let's clarify the mapping between chunks and checkpoints with an example.
 Batches with `batch_idx` >= 1000 are only seen by later checkpoints.
 
 
-*NOTE: 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`).
+*NOTE: 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
@@ -45,7 +48,8 @@ Kudos to [LLM360/AmberDatasets](https://huggingface.co/datasets/LLM360/AmberData
 ## Interacting with the data
 
 Besides clarity and ease of use, another great advantage of this release is that it allows users to easily interact with the data without downloading it.
-The parquet format plays nicely with the Hugging Face Hub. So you can use its integrations with external tools like [DuckDB](https://huggingface.co/docs/hub/en/datasets-duckdb) or [pola-rs](https://huggingface.co/docs/hub/en/datasets-polars) to run queries over the data.
+The parquet format plays nicely with the Hugging Face Hub. 
+So, you can use its integrations with external tools like [DuckDB](https://huggingface.co/docs/hub/en/datasets-duckdb) or [pola-rs](https://huggingface.co/docs/hub/en/datasets-polars) to run queries over the data.
 For example,
 
 ```python