---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- turkish_ner
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: xlm-turkish-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: turkish_ner
      type: turkish_ner
      config: default
      split: train
      args: default
    metrics:
    - name: F1
      type: f1
      value: 0.657840245968501
    - name: Precision
      type: precision
      value: 0.6669776910679447
    - name: Recall
      type: recall
      value: 0.6489497792266744
    - name: Accuracy
      type: accuracy
      value: 0.9113795745182391
---

<!-- 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. -->

# xlm-turkish-ner

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the turkish_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2836
- F1: 0.6578
- Precision: 0.6670
- Recall: 0.6489
- Accuracy: 0.9114

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.2704        | 1.0   | 1250 | 0.2745          | 0.6153 | 0.6250    | 0.6059 | 0.8985   |
| 0.2047        | 2.0   | 2500 | 0.2656          | 0.6372 | 0.6429    | 0.6315 | 0.9046   |
| 0.1646        | 3.0   | 3750 | 0.2628          | 0.6560 | 0.6839    | 0.6303 | 0.9109   |
| 0.1256        | 4.0   | 5000 | 0.2895          | 0.6561 | 0.6641    | 0.6482 | 0.9092   |
| 0.0953        | 5.0   | 6250 | 0.3224          | 0.6555 | 0.6554    | 0.6556 | 0.9088   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0