Update README.md

README.md

@@ -4,31 +4,18 @@ task_categories:
 language:
 - en
 pretty_name: RedPajama Tiny
+license: apache-2.0
+size_categories:
+- n<1K
 ---
 # Dataset Card for Dataset Name
 
 ### Dataset Summary
 
-This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.
-
-This dataset contains 64 samples from each of the 7 sources.
-
-The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T):
-
-| Dataset       | Token Count |
-|---------------|-------------|
-| Commoncrawl   | 878 Billion        |
-| C4            | 175 Billion        |
-| GitHub        | 59 Billion         |
-| Books         | 26 Billion         |
-| ArXiv         | 28 Billion         |
-| Wikipedia     | 24 Billion         |
-| StackExchange | 20 Billion         |
-| Total         | 1.2 Trillion      |
-
-### Languages
-
-Primarily English, though the Wikipedia slice contains multiple languages.
+This is a tiny version of the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). 
+It contains 64 samples from each of the 7 sources.
+This dataset is intended for developing and testing data/training pipeline for loading the full RedPajama dataset or any general HuggingFace dataset. 
+It is very fast to download and easy to examine. You should not use it for training a full model, but you can use it for overfitting test or any other sanity checks. 
 
 ## Dataset Structure
 
@@ -40,42 +27,3 @@ The dataset structure is as follows:
     "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}
 }
 ```
-
-## Dataset Creation
-
-This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.
-
-### Source Data
-
-#### Commoncrawl
-
-We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
-We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
-classify paragraphs as Wikipedia references or random Commoncrawl samples.
-
-#### C4
-
-C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.
-
-#### GitHub
-
-The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
-files and only keep projects that are distributed under the MIT, BSD, or Apache license.
-
-#### Wikipedia
-We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
-text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
-formatting boilerplate has been removed.
-
-#### Gutenberg and Books3
-The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
-simhash to remove near duplicates.
-
-#### ArXiv
-ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and
-remove preambles, comments, macros and bibliographies.
-
-#### Stackexchange
-The Stack Exchange split of the dataset is download from the
-[Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites,
-remove html tags, group the posts into question-answer pairs, and order answers by their score.