akdeniz27/xlm-roberta-base-turkish-ner

Turkish Named Entity Recognition (NER) Model

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).

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 = 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="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: 0.9492100796448622
• precision: 0.9407349896480332
• recall: 0.9578392621870883