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from transformers import Pipeline
import numpy as np
import torch
from nltk.chunk import conlltags2tree
from nltk import pos_tag
from nltk.tree import Tree
import string
import torch.nn.functional as F
import re
from models import ExtendedMultitaskModelForTokenClassification
# Register the custom pipeline
from transformers import pipeline
def tokenize(text):
# print(text)
for punctuation in string.punctuation:
text = text.replace(punctuation, " " + punctuation + " ")
return text.split()
def find_entity_indices(article, entity):
"""
Find all occurrences of an entity in the article and return their indices.
:param article: The complete article text.
:param entity: The entity to search for.
:return: A list of tuples (lArticleOffset, rArticleOffset) for each occurrence.
"""
# normalized_target = normalize_text(entity)
# normalized_document = normalize_text(article)
entity_indices = []
for match in re.finditer(re.escape(entity), article):
start_idx = match.start()
end_idx = match.end()
entity_indices.append((start_idx, end_idx))
return entity_indices
def get_entities(tokens, tags, confidences, text):
tags = [tag.replace("S-", "B-").replace("E-", "I-") for tag in tags]
pos_tags = [pos for token, pos in pos_tag(tokens)]
conlltags = [(token, pos, tg) for token, pos, tg in zip(tokens, pos_tags, tags)]
ne_tree = conlltags2tree(conlltags)
entities = []
idx: int = 0
for subtree in ne_tree:
# skipping 'O' tags
if isinstance(subtree, Tree):
original_label = subtree.label()
original_string = " ".join([token for token, pos in subtree.leaves()])
for indices in find_entity_indices(text, original_string):
entity_start_position = indices[0]
entity_end_position = indices[1]
entities.append(
{
"entity": original_label,
"score": np.average(confidences[idx : idx + len(subtree)]),
"index": idx,
"word": original_string,
"start": entity_start_position,
"end": entity_end_position,
}
)
assert (
text[entity_start_position:entity_end_position] == original_string
)
idx += len(subtree)
# Update the current character position
# We add the length of the original string + 1 (for the space)
else:
token, pos = subtree
# If it's not a named entity, we still need to update the character
# position
idx += 1
return entities
def realign(
text_sentence, out_label_preds, softmax_scores, tokenizer, reverted_label_map
):
preds_list, words_list, confidence_list = [], [], []
word_ids = tokenizer(text_sentence, is_split_into_words=True).word_ids()
for idx, word in enumerate(text_sentence):
beginning_index = word_ids.index(idx)
try:
preds_list.append(reverted_label_map[out_label_preds[beginning_index]])
confidence_list.append(max(softmax_scores[beginning_index]))
except Exception as ex: # the sentence was longer then max_length
preds_list.append("O")
confidence_list.append(0.0)
words_list.append(word)
return words_list, preds_list, confidence_list
class MultitaskTokenClassificationPipeline(Pipeline):
def __init__(self, model, tokenizer, label_map, **kwargs):
super().__init__(model=model, tokenizer=tokenizer, **kwargs)
self.label_map = label_map
self.id2label = {
task: {id_: label for label, id_ in labels.items()}
for task, labels in label_map.items()
}
def _sanitize_parameters(self, **kwargs):
# Add any additional parameter handling if necessary
return kwargs, {}, {}
def preprocess(self, text, **kwargs):
tokenized_inputs = self.tokenizer(
text, padding="max_length", truncation=True, max_length=512
)
text_sentence = tokenize(text)
return tokenized_inputs, text_sentence, text
def _forward(self, inputs):
inputs, text_sentence, text = inputs
input_ids = torch.tensor([inputs["input_ids"]], dtype=torch.long).to(
self.model.device
)
attention_mask = torch.tensor([inputs["attention_mask"]], dtype=torch.long).to(
self.model.device
)
with torch.no_grad():
outputs = self.model(input_ids, attention_mask)
return outputs, text_sentence, text
def postprocess(self, outputs, **kwargs):
"""
Postprocess the outputs of the model
:param outputs:
:param kwargs:
:return:
"""
tokens_result, text_sentence, text = outputs
predictions = {}
confidence_scores = {}
for task, logits in tokens_result.logits.items():
predictions[task] = torch.argmax(logits, dim=-1).tolist()
confidence_scores[task] = F.softmax(logits, dim=-1).tolist()
decoded_predictions = {}
for task, preds in predictions.items():
decoded_predictions[task] = [
[self.id2label[task][label] for label in seq] for seq in preds
]
entities = {}
for task, preds in predictions.items():
words_list, preds_list, confidence_list = realign(
text_sentence,
preds[0],
confidence_scores[task][0],
self.tokenizer,
self.id2label[task],
)
entities[task] = get_entities(words_list, preds_list, confidence_list, text)
return entities
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