bert-base-turkish-uncased-ner

This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on the turkish-wiki_ner dataset. It achieves the following results on the evaluation set:

• Loss: 0.2603
• F1: 0.7821

Model description

This model is a fine-tuned version of dbmdz/bert-base-turkish-uncased on the turkish-wiki_ner dataset. The training dataset consists of 18,967 samples, and the validation dataset consists of 1,000 samples, both derived from Wikipedia data.

For more detailed information, please visit this link: https://huggingface.co/datasets/turkish-nlp-suite/turkish-wikiNER

Labels:

• CARDINAL
• DATE
• EVENT
• FAC
• GPE
• LANGUAGE
• LAW
• LOC
• MONEY
• NORP
• ORDINAL
• ORG
• PERCENT
• PERSON
• PRODUCT
• QUANTITY
• TIME
• TITLE
• WORK_OF_ART

Fine-Tuning Process : https://github.com/saribasmetehan/bert-base-turkish-uncased-ner

Example

from transformers import pipeline
import pandas as pd

text = "Bu toplam sıfır ise, Newton'ın birinci yasası cismin hareket durumunun değişmeyeceğini söyler."
model_id = "saribasmetehan/bert-base-turkish-uncased-ner"
ner = pipeline("ner",model = model_id)
preds= ner(text, aggregation_strategy = "simple")

pd.DataFrame(preds)

Load model directly

from transformers import AutoModelForTokenClassification, AutoTokenizer

model_name = "saribasmetehan/bert-base-turkish-uncased-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1