CNN/DM : data preprocessing
astariul opened this issue · 5 comments
astariul commented
The link to the data of CNN/DM dataset is an already preprocessed dataset.
How can we reproduce similar dataset from the official .story files ?
donglixp commented
step 1: use corenlp to split the sentences
step 2: run BertTokenizer to obtain subword tokens
step 3: save the source text to *.src, and the target text to *.tgt
donglixp commented
@donglixp , can you please provide details regarding how to run these 3 steps?
def process_detokenize(chunk):
twd = TreebankWordDetokenizer()
tokenizer = BertTokenizer.from_pretrained(
args.bert_model, do_lower_case=args.do_lower_case)
r_list = []
for idx, line in chunk:
line = line.strip().replace('``', '"').replace('\'\'', '"').replace('`','\'')
s_list = [twd.detokenize(x.strip().split(
' '), convert_parentheses=True) for x in line.split('<S_SEP>')]
tk_list = [tokenizer.tokenize(s) for s in s_list]
r_list.append((idx, s_list, tk_list))
return r_list
def read_tokenized_file(fn):
with open(fn, 'r', encoding='utf-8') as f_in:
l_list = [l for l in f_in]
num_pool = min(args.processes, len(l_list))
p = Pool(num_pool)
chunk_list = partition_all(
int(len(l_list)/num_pool), list(enumerate(l_list)))
r_list = []
with tqdm(total=len(l_list)) as pbar:
for r in p.imap_unordered(process_detokenize, chunk_list):
r_list.extend(r)
pbar.update(len(r))
p.close()
p.join()
r_list.sort(key=lambda x: x[0])
return [x[1] for x in r_list], [x[2] for x in r_list]
def append_sep(s_list):
r_list = []
for i, s in enumerate(s_list):
r_list.append(s)
r_list.append('[SEP_{0}]'.format(min(9, i)))
return r_list[:-1]
## print('convert into src/tgt format')
with open(os.path.join(args.output_dir, split_out+'.src'), 'w', encoding='utf-8') as f_src, open(os.path.join(args.output_dir, split_out+'.tgt'), 'w', encoding='utf-8') as f_tgt, open(os.path.join(args.output_dir, split_out+'.slv'), 'w', encoding='utf-8') as f_slv:
for src, tgt, lb in tqdm(zip(article_tk, summary_tk, label)):
# source
src_tokenized = [' '.join(s) for s in src]
if args.src_sep_token:
f_src.write(' '.join(append_sep(src_tokenized)))
else:
f_src.write(' '.join(src_tokenized))
f_src.write('\n')
# target (silver)
slv_tokenized = [s for s, extract_flag in zip(
src_tokenized, lb) if extract_flag]
f_slv.write(' [X_SEP] '.join(slv_tokenized))
f_slv.write('\n')
# target (gold)
f_tgt.write(' [X_SEP] '.join(
[' '.join(s) for s in tgt]))
f_tgt.write('\n')The input should have been split by "<S_SEP>".