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

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.

ARMAN

ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.

  1. Organization
  2. Location
  3. Facility
  4. Event
  5. Product
  6. Person
Label #
Organization 30108
Location 12924
Facility 4458
Event 7557
Product 4389
Person 15645

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}
}

Download

How to use

document_assembler = DocumentAssembler() \
    .setInputCol('text') \
    .setOutputCol('document')

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

tokenClassifier = BertForTokenClassification \
      .pretrained('bert_token_classifier_parsbert_armanner', '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_armanner", "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)

Model Information

Model Name: bert_token_classifier_parsbert_armanner
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-armanner-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 |
|---------|----------|------------|--------------|----------|----------------|------------|
| ARMAN   | 93.10*   | 89.9       | 84.03        | 86.55    | -              | 77.45      |