Create README.md

README.md

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+---
+task_categories:
+- text-generation
+language:
+- en
+size_categories:
+- 1M<n<10M
+---
+## LitScan EPMC Subset
+
+This dataset is a subset of [afg1/epmc-oa-subset](https://huggingface.co/datasets/afg1/epmc-oa-subset), 
+which itself comes from the [Europe PMC open access subset](https://europepmc.org/downloads/openaccess) of about 5.9 million articles.
+
+Here, we take the ~960 parquet files from the full OA subset and join them against a list of PMCIDs for articles found by [LitScan](https://rnacentral.org/help/litscan),
+which should discuss ncRNA for the ~9.6 million IDs searched from RNAcentral. The result is a collection of just over 1 million open access
+fulltext articles ostensibly about ncRNA.
+
+The primary use case for this is pre-finetuning on domain specific text. This idea of domain adaptation is similar to what 
+NVIDIA have done with their [ChipNeMo model](https://research.nvidia.com/publication/2023-10_chipnemo-domain-adapted-llms-chip-design).
+
+We are planning to finetune some models on this dataset, probably TinyLlama, since it is quite quick to train. 
+These will be useful for e.g. generating embeddings for RAG, or further downstream finetuning on tasks like summarisation.
+
+## Limitations
+
+The epmc-oa-subset parquet files are parsed from JATS, which does not always go entirely to plan. As a result, there are likely to be some 
+articles with missing text, or strange tags left in. These should be quite rare, but I can't guarantee they're not in there.
+
+LitScan itself also has some limitations, namely that there is quite a high false positive rate for those RNA IDs that are a bit generic. This
+means that while most of the articles in this dataset should be focused on RNA, there will be a significant minority that are about all sorts of
+other things, including but not limited to: concrete, female mice, recurrent neural networks. This is a very tricky problem to solve!
+
+