# -*- coding: utf-8 -*-
# file: data_utils_for_inference.py
# time: 02/11/2022 15:39
# author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# github: https://github.com/yangheng95
# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research
# Copyright (C) 2022. All Rights Reserved.
import tqdm
from torch.utils.data import Dataset
from pyabsa.framework.dataset_class.dataset_template import PyABSADataset
from pyabsa.utils.file_utils.file_utils import load_dataset_from_file
from pyabsa.utils.pyabsa_utils import fprint
[docs]
class BERTRNACInferenceDataset(Dataset):
def __init__(self, config, tokenizer, dataset_type="infer"):
self.config = config
self.tokenizer = tokenizer
self.dataset_type = dataset_type
self.data = []
[docs]
def parse_sample(self, text):
return [text]
[docs]
def prepare_infer_sample(self, text: str, ignore_error):
if isinstance(text, list):
self.process_data(text, ignore_error=ignore_error)
else:
self.process_data(self.parse_sample(text), ignore_error=ignore_error)
[docs]
def prepare_infer_dataset(self, infer_file, ignore_error):
lines = load_dataset_from_file(infer_file, config=self.config)
samples = []
for sample in lines:
if sample:
samples.extend(self.parse_sample(sample))
self.process_data(samples, ignore_error)
[docs]
def process_data(self, samples, ignore_error=True):
all_data = []
if len(samples) > 100:
it = tqdm.tqdm(samples, desc="preparing text classification dataloader")
else:
it = samples
for ex_id, text in enumerate(it):
try:
text, _, label = text.strip().partition("$LABEL$")
# exon1, intron, exon2 = text.split(',')
# label = label.strip() if label else LabelPaddingOption.LABEL_PADDING
# exon1 = exon1.strip()
# intron = intron.strip()
# exon2 = exon2.strip()
# exon1_ids = self.tokenizer.text_to_sequence(exon1, padding='do_not_pad')
# intron_ids = self.tokenizer.text_to_sequence(intron, padding='do_not_pad')
# exon2_ids = self.tokenizer.text_to_sequence(exon2, padding='do_not_pad')
# rna_indices = [self.tokenizer.tokenizer.cls_token_id] + exon1_ids + intron_ids + exon2_ids + [self.tokenizer.tokenizer.sep_token_id]
# rna_indices = pad_and_truncate(rna_indices, self.config.max_seq_len, value=self.tokenizer.pad_token_id)
# intron_indices = self.tokenizer.text_to_sequence(intron)
rna_indices = self.tokenizer.text_to_sequence(text)
data = {
"ex_id": ex_id,
"text_raw": text,
"text_indices": rna_indices,
"label": label,
}
all_data.append(data)
except Exception as e:
if ignore_error:
fprint("Ignore error while processing:", text)
else:
raise e
self.data = all_data
self.data = PyABSADataset.covert_to_tensor(self.data)
return self.data
[docs]
def __getitem__(self, index):
return self.data[index]
[docs]
def __len__(self):
return len(self.data)