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import logging
import os
from typing import List, Tuple
import pandas as pd
import spacy
import torch
from dante_tokenizer import DanteTokenizer
from transformers import AutoModelForTokenClassification, AutoTokenizer
from dotenv import dotenv_values
from dante_tokenizer.data.preprocessing import split_monetary_tokens, normalize_text, split_enclisis
from preprocessing import *
try:
nlp = spacy.load("pt_core_news_sm")
except Exception:
os.system("python -m spacy download pt_core_news_sm")
nlp = spacy.load("pt_core_news_sm")
dt_tokenizer = DanteTokenizer()
model_choices = {
"News": "Emanuel/porttagger-news-base",
"Tweets (stock market)": "Emanuel/porttagger-tweets-base",
"Oil and Gas (academic texts)": "Emanuel/porttagger-oilgas-base",
"Multigenre": "Emanuel/porttagger-base",
}
pre_tokenizers = {
"News": nlp,
"Tweets (stock market)": dt_tokenizer.tokenize,
"Oil and Gas (academic texts)": nlp,
"Multigenre": nlp,
}
env_vars = dotenv_values('.env')
for key, value in env_vars.items():
globals()[key] = value
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
class MyApp:
def __init__(self) -> None:
self.model = None
self.tokenizer = None
self.pre_tokenizer = None
self.load_model()
def load_model(self, model_name: str = DEFAULT_MODEL):
if model_name not in model_choices.keys():
logger.error("Selected model is not supported, resetting to the default model.")
model_name = DEFAULT_MODEL
self.model = AutoModelForTokenClassification.from_pretrained(model_choices[model_name])
self.tokenizer = AutoTokenizer.from_pretrained(model_choices[model_name])
self.pre_tokenizer = pre_tokenizers[model_name]
myapp = MyApp()
def predict(text, logger=None) -> Tuple[List[str], List[str]]:
doc = myapp.pre_tokenizer(text)
tokens = [token.text if not isinstance(token, str) else token for token in doc]
logger.info("Starting predictions for sentence: {}".format(text))
print("Using model {}".format(myapp.model.config.__dict__["_name_or_path"]))
input_tokens = myapp.tokenizer(
tokens,
return_tensors="pt",
is_split_into_words=True,
return_offsets_mapping=True,
return_special_tokens_mask=True,
)
output = myapp.model(input_tokens["input_ids"])
i_token = 0
labels = []
scores = []
for off, is_special_token, pred in zip(
input_tokens["offset_mapping"][0],
input_tokens["special_tokens_mask"][0],
output.logits[0],
):
if is_special_token or off[0] > 0:
continue
label = myapp.model.config.__dict__["id2label"][int(pred.argmax(axis=-1))]
if logger is not None:
logger.info("{}, {}, {}".format(off, tokens[i_token], label))
labels.append(label)
scores.append(
"{:.2f}".format(100 * float(torch.softmax(pred, dim=-1).detach().max()))
)
i_token += 1
return tokens, labels, scores
def batch_analysis_csv(ID_COLUMN: str, CONTENT_COLUMN: str, DATA_PATH: str, PREFIX:str, OUTPUT_PATH: str, KEEP_REPLACE_CONTRACTION: bool):
df = pd.read_csv(DATA_PATH)
ids = df[ID_COLUMN]
texts = df[CONTENT_COLUMN]
texts = texts.replace(r'\\n', ' ', regex=True) # remover '\n' mas não por espaço
texts = texts.apply(lambda x : x.strip()) # remover espaços excedentes
conllu_output = []
for id, sent in zip(ids, texts):
conllu_output.append("# sent_id = {}_{}\n".format(PREFIX, id))
conllu_output.append("# text = {}\n".format(sent))
tokens, labels, _ = predict(sent, logger)
tokens_labels = list(zip(tokens, labels))
for j, (token, label) in enumerate(tokens_labels):
try:
contr = tokens_labels[j][0] + ' ' + tokens_labels[j+1][0]
for expansion in expansions.keys():
replace_str = expansions[expansion]
match = re.match(expansion, contr, re.IGNORECASE)
expansion = replace_keep_case(expansion, replace_str, contr)
if match is not None:
conllu_output.append("{}\t{}".format(str(j+1)+'-'+str(j+2), expansion) + "\t_" * 8 + "\n")
break
conllu_output.append("{}\t{}\t_\t{}".format(j + 1, token, label) + "\t_" * 6 + "\n")
except IndexError:
conllu_output.append("{}\t{}\t_\t{}".format(j + 1, token, label) + "\t_" * 6 + "\n")
conllu_output.append("\n")
with open(OUTPUT_PATH, 'w', encoding='utf-8') as out_f:
out_f.writelines(conllu_output)
def main():
batch_analysis_csv(ID_COLUMN, CONTENT_COLUMN, DATA_PATH, PREFIX, OUTPUT_PATH, KEEP_REPLACE_CONTRACTION)
if __name__ == '__main__':
main() |