| -- | |
| language: tr | |
| --- | |
| # Turkish Named Entity Recognition (NER) Model | |
| This model is the fine-tuned model of dbmdz/bert-base-turkish-cased | |
| 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 = "dbmdz/bert-base-turkish-cased" | |
| 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 = 3 | |
| weight_decay = 0.01 | |
| ``` | |
| # How to use: | |
| ``` | |
| model = AutoModelForTokenClassification.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") | |
| tokenizer = AutoTokenizer.from_pretrained("akdeniz27/bert-base-turkish-cased-ner") | |
| ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") | |
| NER("text") | |
| # Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. | |
| ``` | |
| # Reference test results: | |
| * accuracy: 0.9933935699477056 | |
| * f1: 0.9592969472710453 | |
| * precision: 0.9543530277931161 | |
| * recall: 0.9642923563325274 |