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
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