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---
license: mit
base_model: dbmdz/bert-base-turkish-uncased
tags:
- generated_from_trainer
datasets:
- turkish-wiki_ner
metrics:
- f1
model-index:
- name: bert-base-turkish-uncased-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: turkish-wiki_ner
type: turkish-wiki_ner
config: turkish-WikiNER
split: validation
args: turkish-WikiNER
metrics:
- name: F1
type: f1
value: 0.7821495486288537
language:
- tr
widget:
- text: "Leblebi Mehmet adıyla Galatasarayın sembol futbolcularından oldu."
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-turkish-uncased-ner
This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/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:
<ul>
<li>CARDINAL</li>
<li>DATE</li>
<li>EVENT</li>
<li>FAC</li>
<li>GPE</li>
<li>LANGUAGE</li>
<li>LAW</li>
<li>LOC</li>
<li>MONEY</li>
<li>NORP</li>
<li>ORDINAL</li>
<li>ORG</li>
<li>PERCENT</li>
<li>PERSON</li>
<li>PRODUCT</li>
<li>QUANTITY</li>
<li>TIME</li>
<li>TITLE</li>
<li>WORK_OF_ART</li>
</ul>
Fine-Tuning Process : https://github.com/saribasmetehan/bert-base-turkish-uncased-ner
-
## Example
```markdown
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
```markdown
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
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4 | 1.0 | 1186 | 0.2502 | 0.7703 |
| 0.2227 | 2.0 | 2372 | 0.2439 | 0.7740 |
| 0.1738 | 3.0 | 3558 | 0.2511 | 0.7783 |
| 0.1474 | 4.0 | 4744 | 0.2603 | 0.7821 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |