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# source: https://github.com/mponty/bigcode-dataset/tree/main/pii/ner_model_training/utils by @mponty
import itertools
from tqdm import tqdm
from datasets import Dataset
def is_overlap(span, reference_span):
l1, r1 = min(*span), max(*span)
l2, r2 = min(*reference_span), max(*reference_span)
return l1 <= l2 < r1 or l1 < r2 <= r1 or l2 <= l1 < r2 or l2 < r1 <= r2
def label_tokenized(
entry, target_text="text", pii_column="fragments", LABEL2ID=None, IGNORE_CLASS=None
):
content, pii = entry[target_text], entry[pii_column]
if entry["offset_mapping"][-1] == (0, 0):
entry["offset_mapping"][-1] = (len(content), len(content))
entry["labels"] = [LABEL2ID["O"]] * len(entry["offset_mapping"])
for entity in pii:
if entity["category"] in IGNORE_CLASS:
continue
prefix = "B-"
entity_span = tuple(entity["position"])
for i, span in enumerate(entry["offset_mapping"]):
if is_overlap(entity_span, span):
label = prefix + entity["category"]
entry["labels"][i] = LABEL2ID[label]
prefix = "I-"
return entry
def add_special_toks(entry, target_text, tokenizer):
content = entry[target_text]
entry["input_ids"] = (
[tokenizer.cls_token_id] + entry["input_ids"] + [tokenizer.sep_token_id]
)
entry["attention_mask"] = [1] + entry["attention_mask"] + [1]
entry["offset_mapping"] = (
[(0, 0)] + entry["offset_mapping"] + [(len(content), len(content))]
)
entry["labels"] = [-100] + entry["labels"] + [-100]
return entry
def tokenize_and_label_batch(
entries,
tokenizer,
target_text="text",
pii_column="fragments",
LABEL2ID=None,
IGNORE_CLASS=None,
):
"""Tokenize and label a batch of entries"""
list_inputs = {
k: [] for k in ["input_ids", "attention_mask", "offset_mapping", "labels"]
}
for text, fragments in zip(entries[target_text], entries[pii_column]):
entry = {"text": text, "fragments": fragments}
inputs = tokenizer.encode_plus(
text, return_offsets_mapping=True, add_special_tokens=False
)
entry.update(inputs)
entry = label_tokenized(
entry,
target_text=target_text,
pii_column=pii_column,
LABEL2ID=LABEL2ID,
IGNORE_CLASS=IGNORE_CLASS,
)
entry = add_special_toks(entry, target_text=target_text, tokenizer=tokenizer)
for k in list_inputs.keys():
list_inputs[k].append(entry[k])
return list_inputs
# Chunking
# we do all chunking with overlap_freq = 0
def _get_chunking_step(length, overlap_freq):
step = length
if overlap_freq:
if overlap_freq > 1:
step = length // overlap_freq
else:
step = length // 2
return step
def _chunked_seq(seq, length, overlap_freq=0):
step = _get_chunking_step(length, overlap_freq)
for i in range(len(seq) // step + 1):
if i * step < len(seq):
yield seq[i * step : i * step + length]
def chunk_inputs(
input_ids,
attention_mask,
labels,
id,
*,
tokenizer,
max_length,
overlap_freq=0,
**kwargs
):
chunks = zip(
*[
_chunked_seq(seq, max_length, overlap_freq)
for seq in (input_ids, attention_mask, labels)
]
)
return [
dict(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
id=id,
chunk_id=i,
)
for i, (input_ids, attention_mask, labels) in enumerate(chunks)
]
def chunk_dataset(dataset, tokenizer, overlap_freq=0):
return Dataset.from_list(
list(
itertools.chain(
*(
chunk_inputs(
**entry,
tokenizer=tokenizer,
max_length=tokenizer.model_max_length,
overlap_freq=overlap_freq
)
for entry in tqdm(list(dataset))
)
)
)
)
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