# (1) Import dependencies
import dask.dataframe as dd
import pandas as pd
import torch

# (2) Load pre-trained KGE
kge = torch.load('model.pt')

# (3) Extract/Convert Quaternion-valued entity embeddings From Torch To Numpy 
entity_embeddings=torch.cat((kge['emb_ent_real.weight'].data, kge['emb_ent_i.weight'].data,
kge['emb_ent_j.weight'].data, kge['emb_ent_k.weight'].data),1).detach().numpy()

# (4) Load entity indexes.
entity_to_idx = dd.read_parquet('entity_to_idx.gzip').compute()

# (5) Convert (3) from Numpy narray to Pandas DataFrame
df = dd.from_array(entity_embeddings).compute()

# (6) Set (4) into (5)
df=df.set_index(entity_to_idx.index)

# (7) Enjoy :)
df.head()
                                                         0         1   ...        98        99
http://dbpedia.org/class/yago/'hood108641944       0.391578  1.158492  ...  0.782880  0.709938
http://dbpedia.org/class/yago/11November115185837 -0.250510  0.876174  ...  1.008049  0.312800
http://dbpedia.org/class/yago/14July115200493      0.608239 -0.758226  ...  0.596571  1.071191
http://dbpedia.org/class/yago/1530s115148787       0.647916 -0.089226  ... -0.061985  0.749582
http://dbpedia.org/class/yago/16PF106475933        0.612109 -0.231097  ...  0.597837  0.227571

[5 rows x 100 columns]