Example (from TfidfTransformer)

if isinstance(docs[0], tuple):
    docs = [docs]
return [self.gensim_model[doc] for doc in docs]

This method expects a list of tuples, instead of an iterable. This means that the entire corpus has to be stored as a lis

This is also on page 356.

from nltk.corpus import sentiwordnet as swn

good = swn.senti_synsets('good', 'n')[0]
Traceback (most recent call last):
File "", line 1, in
TypeError: 'filter' object is not subscriptable

fastText supervised model does not take into account of the document and words representation, it just learns bag of words and labels.
embeddings are computed only on the relation word->label. it would be interesting to learn jointly the semantic relation label<->document<->word<->context.
for now it is only possible to pre-train word embeddings and then use them as initial vectors for the clas

Is your feature request related to a problem? Please describe.
When we get predictions we want to write it to storage sink. To contribute a custom storage sink.

Step 1:
I would like you to extend this class
https://github.com/AICoE/log-anomaly-detector/blob/1e25ca878981ece74866be4b8df870d041a61842/anomaly_detector/storage/storage_sink.py#L5-L15

Step 2:
create another function