BERT Token Classification - ParsBERT for Persian Language Understanding (bert_token_classifier_parsbert_peymaner)

Description

ParsBERT: Transformer-based Model for Persian Language Understanding

ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.

Paper presenting ParsBERT: arXiv:2005.12515

All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)

Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]

This task aims to extract named entities in the text, such as names, and label them with appropriate NER classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with IOB format. In this format, tokens that are 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. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, ARMAN, and PEYMA. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.

PEYMA

PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.

Organization
Money
Location
Date
Time
Person
Percent

Label	#
Organization	16964
Money	2037
Location	8782
Date	4259
Time	732
Person	7675
Percent	699

Cite

Please cite the following paper in your publication if you are using ParsBERT in your research:

@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}
}

## Predicted Entities

- B_DAT
- B_LOC
- B_MON
- B_ORG
- B_PCT
- B_PER
- B_TIM
- I_DAT
- I_LOC
- I_MON
- I_ORG
- I_PCT
- I_PER
- I_TIM
- O

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## How to use



<div class="tabs-box" markdown="1">
<div class="tabs-model-aproach-head"><button class="tab-li-model-aproach tabheader_active">Python</button><button class="tab-li-model-aproach">Scala</button><button class="tab-li-model-aproach">NLU</button></div>
```python
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

tokenClassifier = BertForTokenClassification \
.pretrained('bert_token_classifier_parsbert_peymaner', 'fa') \
.setInputCols(['token', 'document']) \
.setOutputCol('ner') \
.setCaseSensitive(False) \
.setMaxSentenceLength(512)

# since output column is IOB/IOB2 style, NerConverter can extract entities
ner_converter = NerConverter() \
.setInputCols(['document', 'token', 'ner']) \
.setOutputCol('entities')

pipeline = Pipeline(stages=[
document_assembler, 
tokenizer,
tokenClassifier,
ner_converter
])

example = spark.createDataFrame([["دفتر مرکزی شرکت کامیکو در شهر ساسکاتون ساسکاچوان قرار دارد."]]).toDF("text")
result = pipeline.fit(example).transform(example)

val document_assembler = DocumentAssembler() 
.setInputCol("text") 
.setOutputCol("document")

val tokenizer = Tokenizer() 
.setInputCols("document") 
.setOutputCol("token")

val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_parsbert_peymaner", "fa")
.setInputCols("document", "token")
.setOutputCol("ner")
.setCaseSensitive(false)
.setMaxSentenceLength(512)

// since output column is IOB/IOB2 style, NerConverter can extract entities
val ner_converter = NerConverter() 
.setInputCols("document", "token", "ner") 
.setOutputCol("entities")

val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, tokenClassifier, ner_converter))

val example = Seq.empty["دفتر مرکزی شرکت کامیکو در شهر ساسکاتون ساسکاچوان قرار دارد."].toDS.toDF("text")

val result = pipeline.fit(example).transform(example)

</div>

Model Information

Model Name:	bert_token_classifier_parsbert_peymaner
Compatibility:	Spark NLP 3.2.0+
License:	Open Source
Edition:	Official
Input Labels:	[token, document]
Output Labels:	[ner]
Language:	fa
Case sensitive:	true
Max sentense length:	512

Data Source

https://huggingface.co/HooshvareLab/bert-base-parsbert-peymaner-uncased

Benchmarking

The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.

| Dataset | ParsBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|---------|----------|------------|--------------|----------|----------------|------------|
| PEYMA   | 98.79*   | -          | 90.59        | -        | 84.00          | -          |

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