File size: 3,970 Bytes
c4b155e fd25542 c4b155e 807e394 c4b155e c3711f2 fd25542 c4b155e fd25542 c4b155e fd25542 c4b155e 807e394 c4b155e 807e394 c4b155e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
---
library_name: transformers
license: mit
base_model: dbmdz/bert-base-turkish-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-ner-turkish-cased
results: []
---
<!-- 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-ner-turkish-cased
This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on a custom Turkish NER dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0987
- Precision: 0.9112
- Recall: 0.9364
- F1: 0.9236
- Accuracy: 0.9600
## Model description
This model identifies named entities in Turkish text:
```python
LABELS = [
"O", "B-PER", "I-PER", "B-LOC", "I-LOC", "B-ORG", "I-ORG",
"B-DATE", "I-DATE", "B-MONEY", "I-MONEY", "B-MISC", "I-MISC"
]
```
- PER: Person
- LOC: Location
- ORG: Organization
- DATE: Date
- MONEY: Money
- MISC: Miscellaneous Entities
## Intended uses & limitations
Extracting entities from Turkish text in NLP pipelines.
## How to Use
```python
from transformers import pipeline
model_name = "yeniguno/bert-ner-turkish-cased"
ner_pipeline = pipeline("ner", model=model_name, tokenizer=model_name, aggregation_strategy="simple")
text = """Selim Parlak, 2023-11-15 tarihinde, CUMHURİYET MAH. DUMAN SOKAK 22500 HAVSA/EDİRNE adresinden, Dünya Varlık Yönetim A.Ş. aracılığıyla 850 TRY değerindeki MP.2386.JPA.IP5.WHT.I İPHONE5 ŞARJLI KILIF "AİR" 1700 MAH (BEYAZ) ürününü satın aldı."""
results = ner_pipeline(text)
for result in results:
print(result)
"""
{'entity_group': 'PER', 'score': 0.9993254, 'word': 'Selim Parlak', 'start': 0, 'end': 12}
{'entity_group': 'DATE', 'score': 0.9987677, 'word': '2023 - 11 - 15', 'start': 14, 'end': 24}
{'entity_group': 'LOC', 'score': 0.99951524, 'word': 'CUMHURİYET MAH. DUMAN SOKAK 22500 HAVSA / EDİRNE', 'start': 36, 'end': 82}
{'entity_group': 'ORG', 'score': 0.8487069, 'word': 'Dünya Varlık Yönetim A. Ş.', 'start': 95, 'end': 120}
{'entity_group': 'MONEY', 'score': 0.9970985, 'word': '850 TRY', 'start': 134, 'end': 141}
{'entity_group': 'MISC', 'score': 0.97721404, 'word': 'MP. 2386. JPA. IP5. WHT. I İPHONE5 ŞARJLI KILIF " AİR " 1700 MAH ( BEYAZ )', 'start': 154, 'end': 219}
"""
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1351 | 1.0 | 1527 | 0.1158 | 0.8592 | 0.9070 | 0.8825 | 0.9517 |
| 0.1088 | 2.0 | 3054 | 0.1045 | 0.8787 | 0.9336 | 0.9053 | 0.9574 |
| 0.1016 | 3.0 | 4581 | 0.0993 | 0.8901 | 0.9280 | 0.9086 | 0.9576 |
| 0.1102 | 4.0 | 6108 | 0.0963 | 0.8991 | 0.9277 | 0.9132 | 0.9587 |
| 0.0877 | 5.0 | 7635 | 0.0953 | 0.9046 | 0.9292 | 0.9167 | 0.9584 |
| 0.0933 | 6.0 | 9162 | 0.0939 | 0.9036 | 0.9321 | 0.9176 | 0.9593 |
| 0.0827 | 7.0 | 10689 | 0.0967 | 0.8986 | 0.9398 | 0.9188 | 0.9605 |
| 0.0933 | 8.0 | 12216 | 0.0949 | 0.9122 | 0.9292 | 0.9206 | 0.9593 |
| 0.084 | 9.0 | 13743 | 0.0987 | 0.9112 | 0.9364 | 0.9236 | 0.9600 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|