File size: 2,654 Bytes
7a675e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- turkish_ner
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: turkish-ner-mBERT-a
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: 0.5209740126867198
- name: Precision
type: precision
value: 0.5447154471544715
- name: Recall
type: recall
value: 0.4992156862745098
- name: Accuracy
type: accuracy
value: 0.8769170049616599
---
<!-- 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. -->
# turkish-ner-mBERT-a
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the turkish_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3741
- F1: 0.5210
- Precision: 0.5447
- Recall: 0.4992
- Accuracy: 0.8769
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| No log | 1.0 | 125 | 0.3508 | 0.4950 | 0.5369 | 0.4592 | 0.8669 |
| No log | 2.0 | 250 | 0.3426 | 0.5253 | 0.5890 | 0.4740 | 0.8757 |
| No log | 3.0 | 375 | 0.3746 | 0.5512 | 0.5718 | 0.5321 | 0.8785 |
| 0.2477 | 4.0 | 500 | 0.4057 | 0.5461 | 0.5302 | 0.5629 | 0.8722 |
| 0.2477 | 5.0 | 625 | 0.4334 | 0.5455 | 0.5393 | 0.5518 | 0.8734 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|