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from src.utils.mapper import configmapper
from transformers import AutoTokenizer
from datasets import load_dataset
# import pdb
@configmapper.map("datasets", "toxic_spans_tokens")
class ToxicSpansTokenDataset:
def __init__(self, config):
# print("### ToxicSpansTokenDataset ###"); exit()
self.config = config
self.tokenizer = AutoTokenizer.from_pretrained(
self.config.model_checkpoint_name
)
# if self.config.model_checkpoint_name == "sberbank-ai/mGPT":
# self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
self.dataset = load_dataset("csv", data_files=dict(self.config.train_files))
self.test_dataset = load_dataset("csv", data_files=dict(self.config.eval_files))
self.tokenized_inputs = self.dataset.map(
self.tokenize_and_align_labels_for_train, batched=True
)
self.test_tokenized_inputs = self.test_dataset.map(
self.tokenize_for_test, batched=True
)
def tokenize_and_align_labels_for_train(self, examples):
tokenized_inputs = self.tokenizer(
examples["text"], **self.config.tokenizer_params
)
# tokenized_inputs["text"] = examples["text"]
example_spans = []
labels = []
offsets_mapping = tokenized_inputs["offset_mapping"]
# pdb.set_trace()
for i, offset_mapping in enumerate(offsets_mapping):
labels.append([])
spans = eval(examples["spans"][i])
example_spans.append(spans)
if self.config.label_cls:
cls_label = (
1
if (
len(examples["text"][i]) > 0
and len(spans) / len(examples["text"][i])
> self.config.cls_threshold
)
else 0
) ## Make class label based on threshold
else:
cls_label = -100
for j, offsets in enumerate(offset_mapping):
if tokenized_inputs["input_ids"][i][j] == self.tokenizer.cls_token_id:
labels[-1].append(cls_label)
elif offsets[0] == offsets[1] and offsets[0] == 0: # All zero
labels[-1].append(-100) ## SPECIAL TOKEN
else:
toxic_offsets = [x in spans for x in range(offsets[0], offsets[1])]
## If any part of the the token is in span, mark it as Toxic
if (
len(toxic_offsets) > 0
and sum(toxic_offsets) / len(toxic_offsets)
> self.config.token_threshold
):
labels[-1].append(1)
else:
labels[-1].append(0)
tokenized_inputs["labels"] = labels
# print("tokenized_inputs", tokenized_inputs); exit()
return tokenized_inputs
def tokenize_for_test(self, examples):
tokenized_inputs = self.tokenizer(
examples["text"], **self.config.tokenizer_params
)
return tokenized_inputs
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