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SmallParallelDocs-Ja_En-6k / datasetChunker.py

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Update datasetChunker.py

a3a7925 verified 4 months ago

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	from transformers import AutoTokenizer
	import jsonlines
	import random
	import os

	tokenizer = AutoTokenizer.from_pretrained("NilanE/tinyllama-relora-merge")

	max_seq_len = 2048 # max context length

	prompt = "Translate this from Japanese to English:\n### JAPANESE: \n### ENGLISH: </s>" # insert SFT prompt to add to token count

	input_file_path = "dataset-parallel-complete.jsonl"

	output_file_path = input_file_path.split('.')[0] + "-chunked." + input_file_path.split('.')[1]
	promptTokens = len(tokenizer.tokenize(prompt))

	def load_jsonl(file_path):
	data = []
	with jsonlines.open(file_path) as reader:
	for entry in reader:
	source = entry['src'].replace('</s>', '').strip()
	target = entry['trg'].replace('</s>', '').strip()
	data.append([source, target])
	return data

	def save_jsonl(file_path, data):
	with jsonlines.open(file_path, 'w') as writer:
	writer.write_all(data)

	chunks = []

	data = load_jsonl(input_file_path)

	#tolerance
	max_seq_len -= 10

	skippedDocs = 0

	for doc in data:

	src_lines = doc[0].split('\n')
	trg_lines = doc[1].split('\n')

	out_src = []
	out_trg = []
	tokenCount = 0
	lastTokenCount = 0
	longLines = 0

	try:
	for x in range(len(src_lines)):
	out_src.append(src_lines[x])
	out_trg.append(trg_lines[x])
	out_src_string = "\n".join(out_src)
	trg_src_string = "\n".join(out_trg)
	tokenCount = len(tokenizer.tokenize(out_src_string.strip() + trg_src_string.strip())) + promptTokens
	if tokenCount-lastTokenCount < max_seq_len-1: # avoid lines > max line length
	if tokenCount > max_seq_len-1:
	src_end = out_src.pop()
	trg_end = out_trg.pop()
	out_src_string = "\n".join(out_src)
	trg_src_string = "\n".join(out_trg)
	data = {
	'src' : out_src_string.strip(),
	'trg' : trg_src_string.strip()
	}
	chunks.append(data)
	out_src = [src_end]
	out_trg = [trg_end]
	elif x+1 == len(src_lines): #and len(out_src) > 2:
	data = {
	'src' : out_src_string.strip(),
	'trg' : trg_src_string.strip()
	}
	chunks.append(data)
	else:
	# remove offending line > max_seq_len
	out_src.pop()
	out_trg.pop()
	out_src_string = "\n".join(out_src)
	trg_src_string = "\n".join(out_trg)
	tokenCount = len(tokenizer.tokenize(out_src_string.strip() + trg_src_string.strip())) + promptTokens
	longLines += 1

	lastTokenCount = tokenCount
	except:
	skippedDocs += 1


	random.shuffle(chunks)

	print(f"LINES LONGER THAN MAX SEQUENCE LENTH: {longLines}")
	print(f"SKIPPED DOCS: {skippedDocs}")

	# Save the randomized data to a new JSONL file
	if os.path.exists(output_file_path):
	os.remove(output_file_path)
	save_jsonl(output_file_path, chunks)