How do I read the cord_19_embeddings_2020-07-16.csv from the COVID-19 Open Research Dataset Challenge (CORD-19) on Kaggle?
So, after a lot of digging, I found something in the comment section.
They are document embeddings.

There is a github repo that specifies an API.

Paper on arxiv

Example usage of a similar approach



Relevant Comments from the Kaggle Comment section on the Data Update Log for the CORD19 Dataset:

Comment 1

Comment 2


Examples how to visualize the embeddings in a Jupyter Notebook:
import pandas as pd
from whatlies import Embedding, EmbeddingSet
#Docs: https://rasahq.github.io/whatlies/api/embeddingset/


#transponse dataframe
sample_df = pd.read_csv('data/cord_embeddings_sample.csv', header=None, delimiter=',', index_col=0).T

def to_ems(df):
    ems_dict = {}
    for columnName, columnData in df.iteritems():
        ems_dict.update({str(columnName): Embedding(columnName, columnData)})
    return EmbeddingSet(ems_dict)

ems = to_ems(sample_df.head(10))

ems.plot_correlation()


ems.plot_interactive(&quot;ug7v899j&quot;,&quot;02tnwd4m&quot;)


You can even do NLP with the jsons from the dataset and link them to the embeddings via the UUID and SHA from metadata.csv.
Example:
Find words that relate to smoking and color the respective papers:
I created 2 EmbeddingSets where I filtered the embeddings for papers that have smoking-related words in their text body and subtracted their UUIDs from the list.
Both EmbeddingSets can be displayed in the plot.
from whatlies.transformers import Umap

# add 2 embedding sets

emb1 = non_smoking_ems.add_property('set', lambda d: 'non smoking papers')
emb2 = smoking_ems.add_property('set', lambda d: 'smoking papers')

both = emb1.merge(emb2)

#add a clustering transformer that reduces dimensionality (like umap) and visualise them
both.transform(Umap(2)).plot_interactive('umap_0', 'umap_1',color='set', annot=False)