---
license: mit
tags:
- generated_from_trainer
datasets:
- ner-tr
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-turkish-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ner-tr
      type: ner-tr
      config: NERTR
      split: train
      args: NERTR
    metrics:
    - name: Precision
      type: precision
      value: 1.0
    - name: Recall
      type: recall
      value: 1.0
    - name: F1
      type: f1
      value: 1.0
    - name: Accuracy
      type: accuracy
      value: 1.0
---

<!-- 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. -->

# distilbert-turkish-ner

This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the ner-tr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0013
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.5744        | 1.0   | 529  | 0.0058          | 1.0       | 1.0    | 1.0 | 1.0      |
| 0.0094        | 2.0   | 1058 | 0.0017          | 1.0       | 1.0    | 1.0 | 1.0      |
| 0.0047        | 3.0   | 1587 | 0.0013          | 1.0       | 1.0    | 1.0 | 1.0      |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1