# Kaggle Competition: Make Data Count

## Competition Objectives

The competition entails the following objectives:

1. Identifies data citations in scientific literature (from full text, e.g. PDFs / XML). In other words: find where a paper mentions some dataset or data resource.
2. Classifies the kind of citation into two categories:
   * Primary: the data was generated or produced as part of the paper’s own study. 
   * Secondary: the data was reused, derived, or taken from elsewhere (existing datasets)

## Kaggle Workspace Repo

{% embed url="<https://github.com/whoamimi/kaggle-kernel/blob/main/make-data-count-workbook-submission.ipynb>" %}

## My Grief

My model plan was vectorizing the research papers and performing Self-organizing maps based on the cosine similarity scores. Given that the dataset's references were listed at random throughout the paper, I thought it would be easier to just segment the papers by some integer, `K`, as primary / secondary link detection. In other words, the training data for one research paper would hold the shape: `[1, K, 384]`

* 1 = batch size
* K = equally segmented data
* 384 = Default hidden dimension size of the `sentence-transformers` encoder model. In this case, `all-MiniLM-L6-v2`  was used and its hidden size is 384 (the dimension of the vector when a sentence is encoded /vectorized i.e. the dimensions of its form represented in latent space)

Unfortunately, ran into server / Kaggle workspace configuration errors so wasn't able to submit the complete model, but the data extraction (objective point 1) was completed.&#x20;

Despite the competition being over, I am still eager on trying. Hopefully, will be able to complete this model when I find the time to.&#x20;


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