Continuous Training / Model tuning / Initialize with pre-trained Model

I want to continue/resume training a pre-trained model using new triples. We may assume that there are no new entities or relations but new instants.

What is the best way to load the pre-trained model?

Or is it possible to implement something like? where continue_training=True is a flag to skip initialization?

import numpy as np
from ampligraph.latent_features import ComplEx

model = ComplEx(batches_count=1, seed=555, epochs=20, k=10)
X = np.array([['a', 'y', 'b'],
              ['b', 'y', 'a'],
              ['a', 'y', 'c'],
              ['c', 'y', 'a'],
              ['a', 'y', 'd'],
              ['c', 'y', 'd'],
              ['b', 'y', 'c'],
              ['f', 'y', 'e']])
model.fit(X)
print(model.get_embeddings(['f','e'], embedding_type='entity'))

X2 = np.array([['a', 'y', 'b'],
              ['c', 'y', 'd'],
              ['b', 'y', 'c'],
              ['f', 'y', 'm'],
              ['f', 'y', 'n'],
              ['f', 'y', 'a'],
              ['f', 'y', 'c'],
               ['f', 'z', 'c']])
model.fit(X2, continue_training=True)

Start at the ComplEx.fit entry point and inspect how model initialization and entity or relation mappings are handled. Define behavior for fitting new triples with existing entities and relations, including the requested continue_training option or pre-trained model loading. Done means the chosen behavior is implemented and verified for the example workflow described in the issue.