diff --git a/parallel_to_m2.py~ b/parallel_to_m2.py~
deleted file mode 100644
index 21a775d..0000000
--- a/parallel_to_m2.py~
+++ /dev/null
@@ -1,68 +0,0 @@
-import argparse
-import os
-import spacy
-from nltk.stem.lancaster import LancasterStemmer
-import scripts.align_text as align_text
-import scripts.cat_rules as cat_rules
-import scripts.toolbox as toolbox
-
-def main(args):
-	# Get base working directory.
-	basename = os.path.dirname(os.path.realpath(__file__))
-	print("Loading resources...")
-	# Load Tokenizer and other resources
-	nlp = spacy.load("en")
-	# Lancaster Stemmer
-	stemmer = LancasterStemmer()
-	# GB English word list (inc -ise and -ize)
-	gb_spell = toolbox.loadDictionary(basename+"/resources/en_GB-large.txt")
-	# Part of speech map file
-	tag_map = toolbox.loadTagMap(basename+"/resources/en-ptb_map")	
-	# Setup output m2 file
-	out_m2 = open(args.out, "w")
-
-	print("Processing files...")
-	# Open the original and corrected text files.
-	with open(args.orig) as orig, open(args.cor) as cor:
-		# Process each pre-aligned sentence pair.
-		for orig_sent, cor_sent in zip(orig, cor):
-			# Write the original sentence to the output m2 file.
-			out_m2.write("S "+orig_sent)
-			# Identical sentences have no edits, so just write noop.
-			if orig_sent == cor_sent:
-				out_m2.write("A -1 -1|||noop|||-NONE-|||REQUIRED|||-NONE-|||0\n")
-			# Otherwise, do extra processing.
-			else:
-				# Markup the parallel sentences with spacy (assume tokenized)
-				proc_orig = toolbox.applySpacy(orig_sent.strip().split(), nlp)
-				proc_cor = toolbox.applySpacy(cor_sent.strip().split(), nlp)
-				# Auto align the parallel sentences and extract the edits.
-				auto_edits = align_text.getAutoAlignedEdits(proc_orig, proc_cor, nlp, args)
-				# Loop through the edits.
-				for auto_edit in auto_edits:
-					# Give each edit an automatic error type.
-					cat = cat_rules.autoTypeEdit(auto_edit, proc_orig, proc_cor, gb_spell, tag_map, nlp, stemmer)
-					auto_edit[2] = cat
-					# Write the edit to the output m2 file.
-					out_m2.write(toolbox.formatEdit(auto_edit)+"\n")
-			# Write a newline when there are no more edits.
-			out_m2.write("\n")
-			
-if __name__ == "__main__":
-	# Define and parse program input
-	parser = argparse.ArgumentParser(description="Convert parallel original and corrected text files (1 sentence per line) into M2 format.\nThe default uses Damerau-Levenshtein and merging rules and assumes tokenized text.",
-								formatter_class=argparse.RawTextHelpFormatter,
-								usage="%(prog)s [-h] [options] -orig ORIG -cor COR -out OUT")
-	parser.add_argument("-orig", help="The path to the original text file.", required=True)
-	parser.add_argument("-cor", help="The path to the corrected text file.", required=True)
-	parser.add_argument("-out",	help="The output filepath.", required=True)						
-	parser.add_argument("-lev",	help="Use standard Levenshtein to align sentences.", action="store_true")
-	parser.add_argument("-merge", choices=["rules", "all-split", "all-merge", "all-equal"], default="rules",
-						help="Choose a merging strategy for automatic alignment.\n"
-								"rules: Use a rule-based merging strategy (default)\n"
-								"all-split: Merge nothing; e.g. MSSDI -> M, S, S, D, I\n"
-								"all-merge: Merge adjacent non-matches; e.g. MSSDI -> M, SSDI\n"
-								"all-equal: Merge adjacent same-type non-matches; e.g. MSSDI -> M, SS, D, I")
-	args = parser.parse_args()
-	# Run the program.
-	main(args)