language: fa
DistilbertNER
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 |
|---|---|---|---|---|
| Distilbert | 0.994534 | 0.946326 | 0.95504 | 0.950663 |
Per entities
| | number | precision | recall | f1 | |:---: |:------: |:---------: |:--------: |:--------: | | DAT | 407 | 0.812048 | 0.828010 | 0.819951 | | EVE | 256 | 0.955056 | 0.996094 | 0.975143 | | FAC | 248 | 0.972549 | 1.000000 | 0.986083 | | LOC | 2884 | 0.968403 | 0.967060 | 0.967731 | | MON | 98 | 0.925532 | 0.887755 | 0.906250 | | ORG | 3216 | 0.932095 | 0.951803 | 0.941846 | | PCT | 94 | 0.936842 | 0.946809 | 0.941799 | | PER | 2645 | 0.959818 | 0.957278 | 0.958546 | | PRO | 318 | 0.963526 | 0.996855 | 0.979907 | | TIM | 43 | 0.760870 | 0.813953 | 0.786517 |
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/distilbert-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.