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# -*- coding: utf-8 -*-
"""
@author:XuMing([email protected])
@description: adjust for chinese tokenizer
"""
import os
import pickle
from multiprocessing import Pool
from datasets import Dataset as HFDataset
from datasets import load_dataset
from torch.utils.data import Dataset
from tqdm.auto import tqdm
from rouge import Rouge
from loguru import logger
def preprocess_batch_for_hf_dataset(dataset, tokenizer, args):
if args.preprocess_inputs:
return tokenizer.prepare_seq2seq_batch(
src_texts=[
prefix + ": " + input_text
for prefix, input_text in zip(dataset["prefix"], dataset["input_text"])
],
tgt_texts=dataset["target_text"],
max_length=args.max_seq_length,
max_target_length=args.max_length,
padding="max_length",
return_tensors="np",
truncation=True,
)
else:
return tokenizer.prepare_seq2seq_batch(
src_texts=[
prefix + input_text
for prefix, input_text in zip(dataset["prefix"], dataset["input_text"])
],
tgt_texts=dataset["target_text"],
max_length=args.max_seq_length,
max_target_length=args.max_length,
padding="max_length",
return_tensors="np",
truncation=True,
)
def load_hf_dataset(data, tokenizer, args):
if isinstance(data, str):
dataset = load_dataset(
"csv",
data_files=data,
delimiter="\t",
download_mode="force_redownload"
if args.reprocess_input_data
else "reuse_dataset_if_exists",
)
else:
dataset = HFDataset.from_pandas(data)
dataset = dataset.map(
lambda x: preprocess_batch_for_hf_dataset(x, tokenizer=tokenizer, args=args),
batched=True,
)
dataset.set_format(type="pt", columns=["input_ids", "attention_mask"])
if isinstance(data, str):
# This is not necessarily a train dataset. The datasets library insists on calling it train.
return dataset["train"]
else:
return dataset
def preprocess_data(data):
prefix, input_text, target_text, tokenizer, args = data
# Add EOS again if truncated?
if args.preprocess_inputs:
batch = tokenizer.prepare_seq2seq_batch(
src_texts=[prefix + ": " + input_text],
tgt_texts=[target_text],
max_length=args.max_seq_length,
padding="max_length",
return_tensors="pt",
truncation=True,
)
else:
batch = tokenizer.prepare_seq2seq_batch(
src_texts=[prefix + ": " + input_text],
tgt_texts=[target_text],
max_length=args.max_seq_length,
padding="max_length",
return_tensors="pt",
truncation=True,
)
input_ids = batch["input_ids"][0]
attention_mask = batch["attention_mask"][0]
labels = batch["labels"][0]
return (input_ids, attention_mask, labels)
class T5Dataset(Dataset):
def __init__(self, tokenizer, args, data, mode):
cached_features_file = os.path.join(
args.cache_dir,
args.model_name.replace("/", "_")
+ "_cached_"
+ str(args.max_seq_length)
+ str(len(data)),
)
if os.path.exists(cached_features_file) and (
(not args.reprocess_input_data and not args.no_cache)
or (mode == "dev" and args.use_cached_eval_features and not args.no_cache)
):
logger.info(" Loading features from cached file %s" % cached_features_file)
with open(cached_features_file, "rb") as handle:
self.examples = pickle.load(handle)
else:
logger.info(" Creating features from dataset file at %s" % args.cache_dir)
data = [
(prefix, input_text, target_text, tokenizer, args)
for prefix, input_text, target_text in zip(
data["prefix"], data["input_text"], data["target_text"]
)
]
if (mode == "train" and args.use_multiprocessing) or (
mode == "dev" and args.use_multiprocessing_for_evaluation
):
if args.multiprocessing_chunksize == -1:
chunksize = max(len(data) // (args.process_count * 2), 500)
else:
chunksize = args.multiprocessing_chunksize
with Pool(args.process_count) as p:
self.examples = list(
tqdm(
p.imap(preprocess_data, data, chunksize=chunksize),
total=len(data),
disable=args.silent,
)
)
else:
self.examples = [preprocess_data(d) for d in tqdm(data, disable=args.silent)]
if not args.no_cache:
logger.info(" Saving features into cached file %s" % cached_features_file)
with open(cached_features_file, "wb") as handle:
pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
def __len__(self):
return len(self.examples)
def __getitem__(self, index):
return self.examples[index]
def dynamic_lcs(X, Y):
# find the length of the strings
m = len(X)
n = len(Y)
# declaring the array for storing the dp values
L = [[None] * (n + 1) for i in range(m + 1)]
"""Following steps build L[m + 1][n + 1] in bottom up fashion
Note: L[i][j] contains length of LCS of X[0..i-1]
and Y[0..j-1]"""
for i in range(m + 1):
for j in range(n + 1):
if i == 0 or j == 0:
L[i][j] = 0
elif X[i - 1] == Y[j - 1]:
L[i][j] = L[i - 1][j - 1] + 1
else:
L[i][j] = max(L[i - 1][j], L[i][j - 1])
# L[m][n] contains the length of LCS of X[0..n-1] & Y[0..m-1]
return L[m][n]
def f1_sim(text_a, text_b):
"""F1相似度
说明：算出两个文本的最长公共子序列长度，然后乘2并处以两者
长度之和。
脚本见：https://github.com/CLUEbenchmark/pCLUE/blob/main/evaluate_pclue.py
计算pCLUE任务总分，及子分数
"""
if not text_a and not text_b:
return 0.
lcs_len = dynamic_lcs(text_a, text_b)
return 2. * lcs_len / (len(text_a) + len(text_b))
def rouge_l_zh(target, pred):
"""计算Rouge-l得分，Rouge-l指标常用于评估自动文本摘要及翻译任务
target: 真实标签
pred: 预测标签"""
if not (isinstance(target, str) or isinstance(pred, str)):
logger.error("target或pred为非字符串！请检查!")
return 0
rouge = Rouge()
scores = rouge.get_scores(" ".join(list(pred)), " ".join(list(target)))
score = scores[0]["rouge-l"]
return score["f"]
if __name__ == '__main__':
a = '123444'
b = '23411'
print(f1_sim(a, b))
print(dynamic_lcs(a, b))
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