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xlm-roberta-base-turkish-ner

Token Classification
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PyTorch
Safetensors
Turkish
xlm-roberta
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language: tr
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  - 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" (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).

Fine-tuning parameters: 

task = "ner"
model_checkpoint = "xlm-roberta-base"
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 
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="first")
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