🚀 Easy-to-use NER Application for Turkish Language
This is an easy-to-use NER (Named Entity Recognition) application for the Turkish language. It's a Python NER model (Bert + Transfer Learning) for Turkish.
📚 Citation
Please cite the following works if you use this model in your study:
@misc{yildirim2024finetuning,
title={Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks},
author={Savas Yildirim},
year={2024},
eprint={2401.17396},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@book{yildirim2021mastering,
title={Mastering Transformers: Build state-of-the-art models from scratch with advanced natural language processing techniques},
author={Yildirim, Savas and Asgari-Chenaghlu, Meysam},
year={2021},
publisher={Packt Publishing Ltd}
}
📦 Installation
Thanks to @stefan-it, the following steps can be used for training:
cd tr-data
for file in train.txt dev.txt test.txt labels.txt
do
wget https://schweter.eu/storage/turkish-bert-wikiann/$file
done
cd ..
This will download the pre-processed datasets with training, development, and test splits and put them in a tr-data
folder.
🔧 Pre-training
After downloading the dataset, pre-training can be started. Just set the following environment variables:
export MAX_LENGTH=128
export BERT_MODEL=dbmdz/bert-base-turkish-cased
export OUTPUT_DIR=tr-new-model
export BATCH_SIZE=32
export NUM_EPOCHS=3
export SAVE_STEPS=625
export SEED=1
Then run pre-training:
python3 run_ner_old.py --data_dir ./tr-data3 \
--model_type bert \
--labels ./tr-data/labels.txt \
--model_name_or_path $BERT_MODEL \
--output_dir $OUTPUT_DIR-$SEED \
--max_seq_length $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \
--per_gpu_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \
--seed $SEED \
--do_train \
--do_eval \
--do_predict \
--fp16
💻 Usage Examples
Basic Usage
from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("savasy/bert-base-turkish-ner-cased")
tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-ner-cased")
ner = pipeline('ner', model=model, tokenizer=tokenizer)
ner("Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a ayak bastı.")
📊 Some Results
Data1: For the data above
Eval Results:
- precision = 0.916400580551524
- recall = 0.9342309684101502
- f1 = 0.9252298787412536
- loss = 0.11335893666411284
Test Results:
- precision = 0.9192058759362955
- recall = 0.9303010230367262
- f1 = 0.9247201697271198
- loss = 0.11182546521618497
Data2:
https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt
The performance for the data given by @kemalaraz is as follows:
savas@savas-lenova:~/Desktop/trans/tr-new-model-1$ cat eval_results.txt
* precision = 0.9461980692049029
* recall = 0.959309358847465
* f1 = 0.9527086063783312
* loss = 0.037054269206847804
savas@savas-lenova:~/Desktop/trans/tr-new-model-1$ cat test_results.txt
* precision = 0.9458370635631155
* recall = 0.9588201928530913
* f1 = 0.952284378344882
* loss = 0.035431676572445225