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--- |
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library_name: transformers |
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license: mit |
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base_model: dbmdz/bert-base-turkish-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: bert-ner-turkish-cased |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-ner-turkish-cased |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0987 |
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- Precision: 0.9112 |
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- Recall: 0.9364 |
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- F1: 0.9236 |
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- Accuracy: 0.9600 |
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## Model description |
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This model identifies named entities in Turkish text: |
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```python |
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LABELS = [ |
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"O", "B-PER", "I-PER", "B-LOC", "I-LOC", "B-ORG", "I-ORG", |
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"B-DATE", "I-DATE", "B-MONEY", "I-MONEY", "B-MISC", "I-MISC" |
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] |
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``` |
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- PER: Person |
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- LOC: Location |
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- ORG: Organization |
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- DATE: Date |
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- MONEY: Money |
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- MISC: Miscellaneous Entities |
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## Intended uses & limitations |
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Extracting entities from Turkish text in NLP pipelines. |
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## How to Use |
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```python |
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from transformers import pipeline |
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model_name = "yeniguno/bert-ner-turkish-cased" |
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ner_pipeline = pipeline("ner", model=model_name, tokenizer=model_name, aggregation_strategy="simple") |
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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.""" |
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results = ner_pipeline(text) |
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for result in results: |
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print(result) |
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""" |
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{'entity_group': 'PER', 'score': 0.9993254, 'word': 'Selim Parlak', 'start': 0, 'end': 12} |
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{'entity_group': 'DATE', 'score': 0.9987677, 'word': '2023 - 11 - 15', 'start': 14, 'end': 24} |
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{'entity_group': 'LOC', 'score': 0.99951524, 'word': 'CUMHURİYET MAH. DUMAN SOKAK 22500 HAVSA / EDİRNE', 'start': 36, 'end': 82} |
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{'entity_group': 'ORG', 'score': 0.8487069, 'word': 'Dünya Varlık Yönetim A. Ş.', 'start': 95, 'end': 120} |
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{'entity_group': 'MONEY', 'score': 0.9970985, 'word': '850 TRY', 'start': 134, 'end': 141} |
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{'entity_group': 'MISC', 'score': 0.97721404, 'word': 'MP. 2386. JPA. IP5. WHT. I İPHONE5 ŞARJLI KILIF " AİR " 1700 MAH ( BEYAZ )', 'start': 154, 'end': 219} |
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""" |
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``` |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-06 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1351 | 1.0 | 1527 | 0.1158 | 0.8592 | 0.9070 | 0.8825 | 0.9517 | |
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| 0.1088 | 2.0 | 3054 | 0.1045 | 0.8787 | 0.9336 | 0.9053 | 0.9574 | |
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| 0.1016 | 3.0 | 4581 | 0.0993 | 0.8901 | 0.9280 | 0.9086 | 0.9576 | |
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| 0.1102 | 4.0 | 6108 | 0.0963 | 0.8991 | 0.9277 | 0.9132 | 0.9587 | |
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| 0.0877 | 5.0 | 7635 | 0.0953 | 0.9046 | 0.9292 | 0.9167 | 0.9584 | |
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| 0.0933 | 6.0 | 9162 | 0.0939 | 0.9036 | 0.9321 | 0.9176 | 0.9593 | |
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| 0.0827 | 7.0 | 10689 | 0.0967 | 0.8986 | 0.9398 | 0.9188 | 0.9605 | |
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| 0.0933 | 8.0 | 12216 | 0.0949 | 0.9122 | 0.9292 | 0.9206 | 0.9593 | |
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| 0.084 | 9.0 | 13743 | 0.0987 | 0.9112 | 0.9364 | 0.9236 | 0.9600 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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