metadata
license: mit
tags:
- generated_from_trainer
datasets:
- ner-tr
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-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
- name: Recall
type: recall
value: 1
- name: F1
type: f1
value: 1
- name: Accuracy
type: accuracy
value: 1
bert-finetuned-ner
This model is a fine-tuned version of dbmdz/bert-base-turkish-cased on the ner-tr dataset. It achieves the following results on the evaluation set:
- Loss: 0.0002
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2603 | 1.0 | 529 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.002 | 2.0 | 1058 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.001 | 3.0 | 1587 | 0.0002 | 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