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from transformers import Pipeline
import numpy as np
import torch
import nltk
nltk.download("averaged_perceptron_tagger")
nltk.download("averaged_perceptron_tagger_eng")
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
label2id = {
"B-org.ent.pressagency.Reuters": 0,
"B-org.ent.pressagency.Stefani": 1,
"O": 2,
"B-org.ent.pressagency.Extel": 3,
"B-org.ent.pressagency.Havas": 4,
"I-org.ent.pressagency.Xinhua": 5,
"I-org.ent.pressagency.Domei": 6,
"B-org.ent.pressagency.Belga": 7,
"B-org.ent.pressagency.CTK": 8,
"B-org.ent.pressagency.ANSA": 9,
"B-org.ent.pressagency.DNB": 10,
"B-org.ent.pressagency.Domei": 11,
"I-pers.ind.articleauthor": 12,
"I-org.ent.pressagency.Wolff": 13,
"B-org.ent.pressagency.unk": 14,
"I-org.ent.pressagency.Stefani": 15,
"I-org.ent.pressagency.AFP": 16,
"B-org.ent.pressagency.UP-UPI": 17,
"I-org.ent.pressagency.ATS-SDA": 18,
"I-org.ent.pressagency.unk": 19,
"B-org.ent.pressagency.DPA": 20,
"B-org.ent.pressagency.AFP": 21,
"I-org.ent.pressagency.DNB": 22,
"B-pers.ind.articleauthor": 23,
"I-org.ent.pressagency.UP-UPI": 24,
"B-org.ent.pressagency.Kipa": 25,
"B-org.ent.pressagency.Wolff": 26,
"B-org.ent.pressagency.ag": 27,
"I-org.ent.pressagency.Extel": 28,
"I-org.ent.pressagency.ag": 29,
"B-org.ent.pressagency.ATS-SDA": 30,
"I-org.ent.pressagency.Havas": 31,
"I-org.ent.pressagency.Reuters": 32,
"B-org.ent.pressagency.Xinhua": 33,
"B-org.ent.pressagency.AP": 34,
"B-org.ent.pressagency.APA": 35,
"I-org.ent.pressagency.ANSA": 36,
"B-org.ent.pressagency.DDP-DAPD": 37,
"I-org.ent.pressagency.TASS": 38,
"I-org.ent.pressagency.AP": 39,
"B-org.ent.pressagency.TASS": 40,
"B-org.ent.pressagency.Europapress": 41,
"B-org.ent.pressagency.SPK-SMP": 42,
}
id2label = {v: k for k, v in label2id.items()}
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):
"""postprocess the outputs here, for example, convert predictions to labels
[
{
"entity": "B-org.ent.pressagency.AFP",
"score": 0.99669313,
"index": 13,
"word": "AF",
"start": 43,
"end": 45,
},
{
"entity": "I-org.ent.pressagency.AFP",
"score": 0.42747754,
"index": 14,
"word": "##P",
"start": 45,
"end": 46,
},
]
[[('AFP', 'org.ent.pressagency.AFP', (12, 13), (47, 50))]]
"""
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.round(
np.average(confidences[idx : idx + len(subtree)]) * 100.0, 2
),
"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):
try:
beginning_index = word_ids.index(idx)
preds_list.append(reverted_label_map[out_label_preds[beginning_index]])
confidence_list.append(softmax_scores[0][beginning_index].max())
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 NewsAgencyModelPipeline(Pipeline):
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
# Get raw logits and convert to numpy array
logits = tokens_result["logits"].detach().cpu().numpy()
# Compute the most likely token ids
tokens_result = np.argmax(logits, axis=2)[0]
# Calculate softmax scores for better interpretability
softmax_scores = F.softmax(torch.from_numpy(logits), dim=-1).numpy()
words_list, preds_list, confidence_list = realign(
text_sentence,
tokens_result,
softmax_scores,
self.tokenizer,
id2label,
)
entities = get_entities(words_list, preds_list, confidence_list, text)
return entities
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