task_categories:
- text-generation
language:
- en
size_categories:
- 1M<n<10M
LitScan EPMC Subset
This dataset is a subset of afg1/epmc-oa-subset, which itself comes from the Europe PMC open access subset 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, 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.
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!