README.md · overfit/twiner-bert-base-mtl at main

ParsTwiNER: Transformer-based Model for Named Entity Recognition at Informal Persian

An open, broad-coverage corpus and model for informal Persian named entity recognition collected from Twitter. Paper presenting ParsTwiNER: 2021.wnut-1.16

Results

The following table summarizes the F1 score on our corpus obtained by ParsTwiNER as compared to ParsBERT as a SoTA for Persian NER.

Named Entity Recognition on Our Corpus

Entity Type	ParsTwiNER F1	ParsBert F1
PER	91	80
LOC	82	68
ORG	69	55
EVE	41	12
POG	85	-
NAT	82.3	-
Total	81.5	69.5

How to use

TensorFlow 2.0

from transformers import TFAutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("overfit/twiner-bert-base-mtl")
model = TFAutoModelForTokenClassification.from_pretrained("overfit/twiner-bert-base-mtl")
twiner_mtl = pipeline('ner', model=model, tokenizer=tokenizer, ignore_labels=[])

Cite

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

@inproceedings{aghajani-etal-2021-parstwiner,
    title = "{P}ars{T}wi{NER}: A Corpus for Named Entity Recognition at Informal {P}ersian",
    author = "Aghajani, MohammadMahdi  and
      Badri, AliAkbar  and
      Beigy, Hamid",
    booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
    month = nov,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.wnut-1.16",
    pages = "131--136",
    abstract = "As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort. ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen{'}s Kappa coefficient, the consistency of annotators is 0.95, a high score. In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture. Experimental results show that the model works well in informal Persian as well as in formal Persian.",
}

Acknowledgments

The authors would like to thank Dr. Momtazi for her support. Furthermore, we would like to acknowledge the accompaniment provided by Mohammad Mahdi Samiei and Abbas Maazallahi.

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