🚀 ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
ALBERT-Persian is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, similar to the approach used for ParsBERT.
Please follow the ALBERT-Persian repo for the latest information about previous and current models.
✨ Features
Persian NER [ARMAN, PEYMA]
This task aims to extract named entities in the text, such as names, and label them with appropriate NER
classes like locations, organizations, etc. The datasets used for this task contain sentences marked in the IOB
format. In this format, tokens not part of an entity are tagged as "O"
, the "B"
tag corresponds to the first word of an object, and the "I"
tag corresponds to the rest of the terms of the same entity. Both "B"
and "I"
tags are followed by a hyphen (or underscore), followed by the entity category. Thus, the NER task is a multi - class token classification problem that labels the tokens when fed a raw text. There are two primary datasets used in Persian NER, ARMAN
and PEYMA
.
ARMAN
The ARMAN dataset contains 7,682 sentences with 250,015 tokens tagged over six different classes:
- Organization
- Location
- Facility
- Event
- Product
- Person
Label |
# |
Organization |
30108 |
Location |
12924 |
Facility |
4458 |
Event |
7557 |
Product |
4389 |
Person |
15645 |
Download
You can download the dataset from here
📚 Documentation
Results
The following table summarizes the F1 score obtained compared to other models and architectures.
Dataset |
ALBERT - fa - base - v2 |
ParsBERT - v1 |
mBERT |
MorphoBERT |
Beheshti - NER |
LSTM - CRF |
Rule - Based CRF |
BiLSTM - CRF |
ARMAN |
97.43 |
98.79 |
95.89 |
89.9 |
84.03 |
86.55 |
- |
77.45 |
BibTeX entry and citation info
Please cite in publications as the following:
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT - Persian: A Lite BERT for Self - supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer - based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
📄 License
This project is licensed under the Apache-2.0 license.
💬 Questions?
Post a Github issue on the ALBERT - Persian repo.