Datasets:
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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)
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