README.md

---
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!