From 242005d762c470a657a8bfe3d728097d08950313 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sava=C5=9F=20Y=C4=B1ld=C4=B1r=C4=B1m?= <savasy@gmail.com>
Date: Fri, 8 May 2020 16:27:29 +0300
Subject: [PATCH] Create README.md (#4132)

* Create README.md

* Adding code fence around code block
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+
+# For Turkish language, here is an easy-to-use NER application. 
+ ** Türkçe için kolay bir python  NER (Bert + Transfer Learning)  (İsim Varlık Tanıma) modeli... 
+
+
+Thanks to @stefan-it, I applied the followings 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 ..
+It will download the pre-processed datasets with training, dev and test splits and put them in a tr-data folder.
+
+Run 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.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 $SEED \
+--do_train \
+--do_eval \
+--do_predict \
+--fp16
+```
+
+
+# Usage
+
+```
+from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
+model = AutoModelForTokenClassification.from_pretrained("savasy/bert-base-turkish-ner-cased")
+tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-ner-cased")
+ner=pipeline('ner', model=model, tokenizer=tokenizer)
+ner("Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a ayak bastı.")
+```
+# Some results
+Data1:  For the data above
+Eval Results:
+
+* precision = 0.916400580551524
+* recall = 0.9342309684101502
+* f1 = 0.9252298787412536
+* loss = 0.11335893666411284
+
+Test Results:
+* precision = 0.9192058759362955
+* recall = 0.9303010230367262
+* f1 = 0.9247201697271198
+* loss = 0.11182546521618497
+
+
+
+Data2:
+https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt
+The performance for the data given by @kemalaraz is as follows
+
+savas@savas-lenova:~/Desktop/trans/tr-new-model-1$ 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
+