diff --git a/systems/parse_spacy3.py b/systems/parse_spacy3.py
new file mode 100644
index 0000000..14e5a9f
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+++ b/systems/parse_spacy3.py
@@ -0,0 +1,123 @@
+import argparse, os
+import spacy
+from spacy.language import Language
+from spacy.tokens import Doc
+import logging, sys, time
+from lib.CoNLL_Annotation import get_token_type
+import my_utils.file_utils as fu
+from germalemma import GermaLemma
+
+
+@Language.factory("my_component")
+class WhitespaceTokenizer(object):
+	def __init__(self, nlp, name):
+		self.vocab = nlp.vocab
+
+	def __call__(self, text):
+		words = text.split(' ')
+		# All tokens 'own' a subsequent space character in this tokenizer
+		spaces = [True] * len(words)
+		return Doc(self.vocab, words=words, spaces=spaces)
+
+
+def get_conll_str(anno_obj, spacy_doc, use_germalemma):
+	#  First lines are comments. (metadata)
+	conll_lines = anno_obj.metadata # Then we want: [ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC]
+	for ix, token in enumerate(spacy_doc):
+		if use_germalemma == "True":
+			content = (str(ix), token.text, find_germalemma(token.text, token.tag_, token.lemma_), token.pos_, token.tag_, "_", "_", "_", "_", "_")
+		else:
+			content = (str(ix), token.text, token.lemma_, token.pos_, token.tag_, "_", "_", "_", "_", "_") # Pure SpaCy!
+		conll_lines.append("\t".join(content))
+	return "\n".join(conll_lines)
+
+
+# def freeling_lemma_lookup():
+# 	dicts_path = "/home/daza/Frameworks/FreeLing/data/de/dictionary/entries/"
+	
+def find_germalemma(word, pos, spacy_lemma):
+	simplify_pos = {"ADJA":"ADJ", "ADJD":"ADJ",
+					"NA":"N", "NE":"N", "NN":"N",
+					"ADV":"ADV", "PAV":"ADV", "PROAV":"ADV", "PAVREL":"ADV", "PWAV":"ADV", "PWAVREL":"ADV",
+					"VAFIN":"V", "VAIMP":"V", "VAINF":"V", "VAPP":"V", "VMFIN":"V", "VMINF":"V",
+					"VMPP":"V", "VVFIN":"V", "VVIMP":"V", "VVINF":"V", "VVIZU":"V","VVPP":"V"
+				}
+	# simplify_pos = {"VERB": "V", "ADV": "ADV", "ADJ": "ADJ", "NOUN":"N", "PROPN": "N"}
+	try:
+		return lemmatizer.find_lemma(word, simplify_pos.get(pos, "UNK"))
+	except:
+		return spacy_lemma
+
+
+if __name__ == "__main__":
+	"""
+		EXAMPLE:
+		--- TIGER Classic Orthography ---
+			python systems/parse_spacy3.py --corpus_name TigerTestNew \
+				-i /home/daza/datasets/TIGER_conll/data_splits/test/Tiger.NewOrth.test.conll \
+				-o /home/daza/datasets/TIGER_conll/tiger_spacy3_parsed.conllu
+	"""
+	
+	parser = argparse.ArgumentParser()
+	parser.add_argument("-i", "--input_file", help="Input Corpus", required=True)
+	parser.add_argument("-n", "--corpus_name", help="Corpus Name", default="Corpus")
+	parser.add_argument("-o", "--output_file", help="File where the Predictions will be saved", required=True)
+	parser.add_argument("-sm", "--spacy_model", help="Spacy model containing the pipeline to tag", default="de_core_news_sm")
+	parser.add_argument("-gtt", "--gld_token_type", help="CoNLL Format of the Gold Data", default="CoNLLUP_Token")
+	parser.add_argument("-ugl", "--use_germalemma", help="Use Germalemma lemmatizer on top of SpaCy", default="True")
+	parser.add_argument("-c", "--comment_str", help="CoNLL Format of comentaries inside the file", default="#")
+	args = parser.parse_args()
+	
+	file_has_next, chunk_ix = True, 0
+	CHUNK_SIZE = 1000
+	SPACY_BATCH = 100
+	SPACY_PROC = 4
+	
+	# =====================================================================================
+	#                    LOGGING INFO ...
+	# =====================================================================================
+	logger = logging.getLogger(__name__)
+	console_hdlr = logging.StreamHandler(sys.stdout)
+	file_hdlr = logging.FileHandler(filename=f"logs/Parse_{args.corpus_name}.SpaCy.log")
+	logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr])
+	logger.info(f"Chunking {args.corpus_name} Corpus in chunks of {CHUNK_SIZE} Sentences")
+	
+	# =====================================================================================
+	#                    POS TAG DOCUMENTS
+	# =====================================================================================
+	
+	if os.path.exists(args.spacy_model):
+		pass # Load Custom Trained model
+	else:
+		# try:
+		spacy_de = spacy.load(args.spacy_model, disable=["ner", "parser"])
+		spacy_de.tokenizer = WhitespaceTokenizer(spacy_de, "keep_original_tokens") # We won't re-tokenize to respect how the source CoNLL are tokenized!
+		# except:
+		# 	print(f"Check if model {args.spacy_model} is a valid SpaCy Pipeline or if the Path containing the trained model exists!")
+		# 	exit()
+	
+	write_out = open(args.output_file, "w")
+	lemmatizer = GermaLemma()
+	
+	if ".gz" == args.input_file[-3:]:
+		in_file = fu.expand_file(args.input_file)
+	else:
+		in_file = args.input_file
+	
+	start = time.time()
+	total_processed_sents = 0
+	line_generator = fu.file_generator(in_file)
+	while file_has_next:
+		annos, file_has_next = fu.get_file_annos_chunk(line_generator, chunk_size=CHUNK_SIZE, 		token_class=get_token_type(args.gld_token_type), comment_str=args.comment_str)
+		if len(annos) == 0: break
+		total_processed_sents += len(annos)
+		logger.info(f"Already processed {total_processed_sents} sentences...")
+		sents = [a.get_sentence() for a in annos]
+		for ix, doc in enumerate(spacy_de.pipe(sents, batch_size=SPACY_BATCH, n_process=SPACY_PROC)):
+			conll_str = get_conll_str(annos[ix], doc, use_germalemma=args.use_germalemma)
+			write_out.write(conll_str)
+			write_out.write("\n\n")
+			
+	end = time.time()
+	logger.info(f"Processing {args.corpus_name} took {(end - start)} seconds!")
+			
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