Convert CONLL file to a list of Doc objects - spacy

Question

Is there a way to convert CONLL file into list of Doc objects without having to parse the sentence using the nlp object. I have a list of annotations that I have to pass to the automatic component that uses Doc objects as input. I have found a way to create the doc:
doc = Doc(nlp.vocab, words=[...])
And that I can use the from_array function to recreate the other linguistic features. This array can be recreated by using index value from StringStore object, I have successfully created Doc object with LEMMA and TAG information but cannot recreate HEAD data. My question is how to pass HEAD data to Doc object using from_array method.
The confusing thing about the HEAD is that for sentence that has this structure:
Ona 2
je 2
otišla 2
u 4
školu 2
. 2
The output of this code snippet:
from spacy.attrs import TAG, HEAD, DEP
doc.to_array([TAG, HEAD, DEP])
is:
array([[10468770234730083819, 2, 429],
[ 5333907774816518795, 1, 405],
[11670076340363994323, 0, 8206900633647566924],
[ 6471273018469892813, 1, 8110129090154140942],
[ 7055653905424136462, 18446744073709551614, 435],
[ 7173976090571422945, 18446744073709551613, 445]],
dtype=uint64)
I cannot correlate the center column of the from_array output to dependency tree structure given above.
Thanks in advance for the help,
Daniel

Answer 1

Ok, so I finally cracked it it appears that head - index if the index is lower than head and 18446744073709551616 - index otherwise. This is the function that I used if anyone else needed it:
import numpy as np
from spacy.tokens import Doc
docs = []
for sent in sents:
generated_doc = Doc(doc.vocab, words=[word["word"] for word in sent])
heads = []
for idx, word in enumerate(sent):
if word["pos"] not in doc.vocab.strings:
doc.vocab.strings.add(word["pos"])
if word["dep"] not in doc.vocab.strings:
doc.vocab.strings.add(word["dep"])
if word["head"] >= idx:
heads.append(word["head"] - idx)
else:
heads.append(18446744073709551616-idx)
np_array = np.array([np.array([doc.vocab.strings[word["pos"]], heads[idx], doc.vocab.strings[word["dep"]]], dtype=np.uint64) for idx, word in enumerate(sent)], dtype=np.uint64)
generated_doc.from_array([TAG, HEAD, DEP], np_array)
docs.append(generated_doc)