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Huseyin
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bert-base-uncased-finetuned-ner

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  1. README.md +10 -10
  2. model.safetensors +1 -1
  3. runs/Jun07_19-32-13_4dc2854032a6/events.out.tfevents.1717788734.4dc2854032a6.35872.0 +2 -2
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2128
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- - Precision: 0.9438
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- - Recall: 0.9453
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- - F1: 0.9446
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- - Accuracy: 0.9412
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.5739 | 1.0 | 625 | 0.2622 | 0.9206 | 0.9248 | 0.9227 | 0.9207 |
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- | 0.2568 | 2.0 | 1250 | 0.2129 | 0.9382 | 0.9452 | 0.9417 | 0.9385 |
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- | 0.1837 | 3.0 | 1875 | 0.2128 | 0.9438 | 0.9453 | 0.9446 | 0.9412 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2076
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+ - Precision: 0.9531
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+ - Recall: 0.9556
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+ - F1: 0.9544
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+ - Accuracy: 0.9468
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3298 | 1.0 | 1250 | 0.2486 | 0.9328 | 0.9362 | 0.9345 | 0.9253 |
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+ | 0.1802 | 2.0 | 2500 | 0.2049 | 0.9489 | 0.9552 | 0.9520 | 0.9448 |
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+ | 0.1233 | 3.0 | 3750 | 0.2076 | 0.9531 | 0.9556 | 0.9544 | 0.9468 |
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  ### Framework versions
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