File size: 2,552 Bytes
81a8a6e a3864e2 81a8a6e | 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 | from datasets import load_metric
import datasets
from transformers import AutoTokenizer
from hf_dataset import JPNDataset
metric = load_metric("seqeval")
logger = datasets.logging.get_logger(__name__)
class HFTokenizer(object):
NAME = "HFTokenizer"
def __init__(self,
hf_pretrained_tokenizer_checkpoint):
self._tokenizer = AutoTokenizer.from_pretrained(hf_pretrained_tokenizer_checkpoint)
@property
def tokenizer(self):
return self._tokenizer
@staticmethod
def init_vf(hf_pretrained_tokenizer_checkpoint):
return HFTokenizer(hf_pretrained_tokenizer_checkpoint=hf_pretrained_tokenizer_checkpoint)
def tokenize_and_align_labels(self,
examples,
label_all_tokens=True):
tokenized_inputs = self._tokenizer(examples["tokens"],
truncation=True,
is_split_into_words=True)
labels = []
for i, label in enumerate(examples["ner_tags"]):
word_ids = tokenized_inputs.word_ids(batch_index=i)
previous_word_idx = None
label_ids = []
for word_idx in word_ids:
# Special tokens have a word id that is None. We set the label to -100 so they are automatically
# ignored in the loss function.
if word_idx is None:
label_ids.append(-100)
# We set the label for the first token of each word.
elif word_idx != previous_word_idx:
label_ids.append(label[word_idx])
# For the other tokens in a word, we set the label to either the current label or -100, depending on
# the label_all_tokens flag.
else:
label_ids.append(label[word_idx] if label_all_tokens else -100)
previous_word_idx = word_idx
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs
if __name__ == '__main__':
hf_pretrained_tokenizer_checkpoint = "distilbert-base-uncased"
dataset = JPNDataset().dataset
hf_preprocessor = HFTokenizer.init_vf(hf_pretrained_tokenizer_checkpoint=hf_pretrained_tokenizer_checkpoint)
tokenized_datasets = dataset.map(hf_preprocessor.tokenize_and_align_labels, batched=True)
print("First sample: ", dataset['train'][0])
#print("First tokenized sample: ", tokenized_datasets['train'][0])
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