def test_issue599(en_vocab):
doc = Doc(en_vocab)
doc.is_tagged = True
doc.is_parsed = True
doc2 = Doc(doc.vocab)
doc2.from_bytes(doc.to_bytes())
assert doc2.is_parsed
def test_serialize_transformer_data():
data = {"x": TransformerData.empty()}
bytes_data = srsly.msgpack_dumps(data)
new_data = srsly.msgpack_loads(bytes_data)
assert isinstance(new_data["x"], TransformerData)
nlp = Language()
trf = nlp.add_pipe(
"transformer",
config={
"model": {
"name": "distilbert-base-uncased",
"transformer_config": {
"output_attentions": True
},
}
},
)
nlp.initialize()
doc = nlp("This is a test.")
b = doc.to_bytes()
reloaded_doc = Doc(nlp.vocab)
reloaded_doc.from_bytes(b)
assert_docs_equal(doc, reloaded_doc)
for key in doc._.trf_data.model_output:
assert_array_equal(doc._.trf_data.model_output[key],
reloaded_doc._.trf_data.model_output[key])
def test_issue599(en_vocab):
doc = Doc(en_vocab)
doc.is_tagged = True
doc.is_parsed = True
doc2 = Doc(doc.vocab)
doc2.from_bytes(doc.to_bytes())
assert doc2.is_parsed
def test_serialize_empty_doc(en_vocab):
doc = Doc(en_vocab)
data = doc.to_bytes()
doc2 = Doc(en_vocab)
doc2.from_bytes(data)
assert len(doc) == len(doc2)
for token1, token2 in zip(doc, doc2):
assert token1.text == token2.text
def test_serialize_empty_doc(en_vocab):
doc = Doc(en_vocab)
data = doc.to_bytes()
doc2 = Doc(en_vocab)
doc2.from_bytes(data)
assert len(doc) == len(doc2)
for token1, token2 in zip(doc, doc2):
assert token1.text == token2.text
def test_issue599(en_vocab):
doc = Doc(en_vocab)
doc2 = Doc(doc.vocab)
doc2.from_bytes(doc.to_bytes())
assert doc2.has_annotation("DEP")
nlp = spacy.load("en_core_web_sm")
doc_base = nlp(text)
print("")
print_doc(doc_base)
# Serialize document to disk and bytes
doc_base.to_disk("doc.spacy")
doc_base_bytes = doc_base.to_bytes()
# Serialize using DocBin
docbin_base = DocBin(attrs=["ENT_IOB", "POS", "HEAD", "DEP", "ENT_TYPE"], store_user_data=True)
docbin_base.add(doc_base)
docbin_base_bytes = docbin_base.to_bytes()
# Restore document from disk
doc = Doc(Vocab())
doc.from_disk("doc.spacy")
print("")
print_doc(doc)
# Restore document from bytes
doc = Doc(Vocab())
doc.from_bytes(doc_base_bytes)
print("")
print_doc(doc)
# Restore using DocBin
docbin = DocBin().from_bytes(docbin_base_bytes)
docs = list(docbin.get_docs(nlp.vocab))
print("")
print_doc(docs[0])
