Update README.md

README.md

@@ -4,7 +4,7 @@ widget:
 - text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı."
 ---
 # Turkish Named Entity Recognition (NER) Model
-This model is the fine-tuned model of "xlm-roberta-base"
+This model is the fine-tuned version of "xlm-roberta-base"
 (a multilingual version of RoBERTa) 
 using a reviewed version of well known Turkish NER dataset 
 (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt).
@@ -16,19 +16,19 @@ batch_size = 8
 label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']
 max_length = 512 
 learning_rate = 2e-5 
-num_train_epochs = 4 
+num_train_epochs = 2 
 weight_decay = 0.01 
 ```
 # How to use: 
 ```
 model = AutoModelForTokenClassification.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner")
 tokenizer = AutoTokenizer.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner")
-ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="none")
+ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
 ner("<your text here>")
 ```
 Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter.
 # Reference test results:
 * accuracy: 0.9919343118732742
-* f1: 0.945422814532762
-* precision: 0.9366551398931153
-* recall: 0.9543561819346573
+* f1: 0.9492100796448622
+* precision: 0.9407349896480332
+* recall: 0.9578392621870883