roberta-fa-zwnj-base-ner

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

Evaluation

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

Overall

Per entities

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.