91 lines
2.4 KiB
Markdown
91 lines
2.4 KiB
Markdown
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# For Turkish language, here is an easy-to-use NER application.
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** Türkçe için kolay bir python NER (Bert + Transfer Learning) (İsim Varlık Tanıma) modeli...
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Thanks to @stefan-it, I applied the followings for training
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cd tr-data
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for file in train.txt dev.txt test.txt labels.txt
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do
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wget https://schweter.eu/storage/turkish-bert-wikiann/$file
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done
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cd ..
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It will download the pre-processed datasets with training, dev and test splits and put them in a tr-data folder.
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Run pre-training
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After downloading the dataset, pre-training can be started. Just set the following environment variables:
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```
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export MAX_LENGTH=128
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export BERT_MODEL=dbmdz/bert-base-turkish-cased
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export OUTPUT_DIR=tr-new-model
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export BATCH_SIZE=32
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export NUM_EPOCHS=3
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export SAVE_STEPS=625
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export SEED=1
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```
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Then run pre-training:
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```
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python3 run_ner.py --data_dir ./tr-data3 \
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--model_type bert \
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--labels ./tr-data/labels.txt \
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--model_name_or_path $BERT_MODEL \
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--output_dir $OUTPUT_DIR-$SEED \
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--max_seq_length $MAX_LENGTH \
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--num_train_epochs $NUM_EPOCHS \
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--per_gpu_train_batch_size $BATCH_SIZE \
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--save_steps $SAVE_STEPS \
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--seed $SEED \
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--do_train \
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--do_eval \
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--do_predict \
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--fp16
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```
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# Usage
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```
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from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
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model = AutoModelForTokenClassification.from_pretrained("savasy/bert-base-turkish-ner-cased")
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tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-ner-cased")
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ner=pipeline('ner', model=model, tokenizer=tokenizer)
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ner("Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a ayak bastı.")
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```
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# Some results
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Data1: For the data above
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Eval Results:
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* precision = 0.916400580551524
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* recall = 0.9342309684101502
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* f1 = 0.9252298787412536
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* loss = 0.11335893666411284
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Test Results:
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* precision = 0.9192058759362955
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* recall = 0.9303010230367262
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* f1 = 0.9247201697271198
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* loss = 0.11182546521618497
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Data2:
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https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt
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The performance for the data given by @kemalaraz is as follows
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savas@savas-lenova:~/Desktop/trans/tr-new-model-1$ cat eval_results.txt
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* precision = 0.9461980692049029
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* recall = 0.959309358847465
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* f1 = 0.9527086063783312
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* loss = 0.037054269206847804
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savas@savas-lenova:~/Desktop/trans/tr-new-model-1$ cat test_results.txt
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* precision = 0.9458370635631155
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* recall = 0.9588201928530913
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* f1 = 0.952284378344882
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* loss = 0.035431676572445225
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