Spaces:
Running
Running
File size: 7,146 Bytes
da060de |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
# -*- 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))
|