---
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