Update README.md

README.md

@@ -16,4 +16,50 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+pretty_name: OSCAR 2023.1 subset
+license: cc0-1.0
+multilinguality:
+- multilingual
+source_datasets:
+- oscar-corpus/OSCAR-2301
+task_categories:
+- fill-mask
+- text-generation
+task_ids:
+- language-modeling
+paperswithcode_id: oscar
+extra_gated_prompt: >-
+  By filling the form below, you understand that only the metadata and the
+  annotations of OSCAR 23.01 have a cc0-1.0 license, and that the rest of the
+  content is crawled data derived from the November/December 2022 snapshot of
+  Common Crawl, for which the authors of OSCAR **do not** hold any copyright
+  whatsoever.
+extra_gated_fields:
+  Name: text
+  Email: text
+  Affiliation: text
+  Country: text
+  Usecase: text
+  I have explicitly check with my jurisdiction and I confirm that downloading OSCAR 2301 is legal in the country/region where I am located right now, and for the use case that I have described above: checkbox
+tags:
+- oscar
 ---
+
+This dataset is a subset of [OSCAR
+2023.1](https://oscar-project.github.io/documentation/versions/oscar-2301/)
+obtained by sampling randomly 50% of documents from the first 30 JSONL files
+for each language contained in the mother corpus, followed by truncating each
+document to the first 2048 Unicode code points. It thus contains all languages
+in OSCAR but drastically oversamples less frequent languages in comparison to
+larger ones.
+
+### Languages
+
+For convenience the languages all files are shipped in a single folder and can
+be loaded together without manually loading invidividual languages.
+
+### Supported Tasks
+
+This dataset is primarily intended for pretraining multilingual tiny language
+models with limited context length (~2048 for tokenization-free byte
+embeddings) such as [ByteLlama](https://github.com/mittagessen/bytellama).