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RobertaNER

This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from ARMAN, PEYMA, and WikiANN that covered ten types of entities:

  • Date (DAT)
  • Event (EVE)
  • Facility (FAC)
  • Location (LOC)
  • Money (MON)
  • Organization (ORG)
  • Percent (PCT)
  • Person (PER)
  • Product (PRO)
  • Time (TIM)

Dataset Information

Records B-DAT B-EVE B-FAC B-LOC B-MON B-ORG B-PCT B-PER B-PRO B-TIM I-DAT I-EVE I-FAC I-LOC I-MON I-ORG I-PCT I-PER I-PRO I-TIM
Train 29133 1423 1487 1400 13919 417 15926 355 12347 1855 150 1947 5018 2421 4118 1059 19579 573 7699 1914 332
Valid 5142 267 253 250 2362 100 2651 64 2173 317 19 373 799 387 717 270 3260 101 1382 303 35
Test 6049 407 256 248 2886 98 3216 94 2646 318 43 568 888 408 858 263 3967 141 1707 296 78

Evaluation

The following tables summarize the scores obtained by model overall and per each class.

Overall

Model accuracy precision recall f1
Roberta 0.994849 0.949816 0.960235 0.954997

Per entities

number precision recall f1
DAT 407 0.844869 0.869779 0.857143
EVE 256 0.948148 1.000000 0.973384
FAC 248 0.957529 1.000000 0.978304
LOC 2884 0.965422 0.968100 0.966759
MON 98 0.937500 0.918367 0.927835
ORG 3216 0.943662 0.958333 0.950941
PCT 94 1.000000 0.968085 0.983784
PER 2646 0.957030 0.959562 0.958294
PRO 318 0.963636 1.000000 0.981481
TIM 43 0.739130 0.790698 0.764045

How To Use

You use this model with Transformers pipeline for NER.

Installing requirements

pip install transformers

How to predict using pipeline

from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification  # for pytorch
from transformers import TFAutoModelForTokenClassification  # for tensorflow
from transformers import pipeline


model_name_or_path = "HooshvareLab/roberta-fa-zwnj-base-ner" 
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path)  # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path)  # Tensorflow

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."

ner_results = nlp(example)
print(ner_results)

Questions?

Post a Github issue on the ParsNER Issues repo.

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