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yanekyuk
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berturk-uncased-keyword-discriminator

Token Classification
Transformers
PyTorch
Turkish
bert
Generated from Trainer
Inference Endpoints
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+ ---
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+ license: mit
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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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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: berturk-uncased-keyword-discriminator
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+ results: []
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+ ---
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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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+
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+ # berturk-uncased-keyword-discriminator
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+
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+ This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3989
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+ - Precision: 0.6234
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+ - Recall: 0.6508
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+ - Accuracy: 0.9145
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+ - F1: 0.6368
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+ - Ent/precision: 0.6435
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+ - Ent/accuracy: 0.7120
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+ - Ent/f1: 0.6761
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+ - Con/precision: 0.5834
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+ - Con/accuracy: 0.5475
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+ - Con/f1: 0.5649
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+ - num_epochs: 8
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
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+ | 0.2005 | 1.0 | 1875 | 0.2104 | 0.5981 | 0.5978 | 0.9148 | 0.5979 | 0.6280 | 0.6665 | 0.6467 | 0.5383 | 0.4820 | 0.5086 |
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+ | 0.1468 | 2.0 | 3750 | 0.2094 | 0.5996 | 0.6568 | 0.9164 | 0.6269 | 0.6285 | 0.7049 | 0.6645 | 0.5477 | 0.5757 | 0.5614 |
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+ | 0.1124 | 3.0 | 5625 | 0.2372 | 0.6106 | 0.6679 | 0.9154 | 0.6380 | 0.6285 | 0.7270 | 0.6741 | 0.5753 | 0.5684 | 0.5718 |
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+ | 0.0861 | 4.0 | 7500 | 0.2736 | 0.6133 | 0.6707 | 0.9145 | 0.6407 | 0.6281 | 0.7359 | 0.6777 | 0.5830 | 0.5606 | 0.5716 |
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+ | 0.0644 | 5.0 | 9375 | 0.3081 | 0.6115 | 0.6683 | 0.9145 | 0.6386 | 0.6291 | 0.7293 | 0.6755 | 0.5764 | 0.5657 | 0.5710 |
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+ | 0.0498 | 6.0 | 11250 | 0.3449 | 0.6245 | 0.6466 | 0.9149 | 0.6353 | 0.6380 | 0.7097 | 0.6720 | 0.5965 | 0.5401 | 0.5669 |
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+ | 0.0401 | 7.0 | 13125 | 0.3838 | 0.6223 | 0.6545 | 0.9140 | 0.6380 | 0.6449 | 0.7100 | 0.6759 | 0.5790 | 0.5610 | 0.5699 |
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+ | 0.0329 | 8.0 | 15000 | 0.3989 | 0.6234 | 0.6508 | 0.9145 | 0.6368 | 0.6435 | 0.7120 | 0.6761 | 0.5834 | 0.5475 | 0.5649 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.19.2
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.2.2
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+ - Tokenizers 0.12.1