AlbertNER
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 |
|---|---|---|---|---|
| Albert | 0.993405 | 0.938907 | 0.943966 | 0.941429 |
Per entities
| | number | precision | recall | f1 | |:---: |:------: |:---------: |:--------: |:--------: | | DAT | 407 | 0.820639 | 0.820639 | 0.820639 | | EVE | 256 | 0.936803 | 0.984375 | 0.960000 | | FAC | 248 | 0.925373 | 1.000000 | 0.961240 | | LOC | 2884 | 0.960818 | 0.960818 | 0.960818 | | MON | 98 | 0.913978 | 0.867347 | 0.890052 | | ORG | 3216 | 0.920892 | 0.937500 | 0.929122 | | PCT | 94 | 0.946809 | 0.946809 | 0.946809 | | PER | 2644 | 0.960000 | 0.944024 | 0.951945 | | PRO | 318 | 0.942943 | 0.987421 | 0.964670 | | TIM | 43 | 0.780488 | 0.744186 | 0.761905 |
How To Use
You use this model with Transformers pipeline for NER.
Installing requirements
pip install sentencepiece
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/albert-fa-zwnj-base-v2-ner" # Albert
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.
- Downloads last month
- 23