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yanekyuk
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berturk-cased-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-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-keyword-discriminator
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+
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4196
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+ - Precision: 0.6729
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+ - Recall: 0.6904
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+ - Accuracy: 0.9163
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+ - F1: 0.6815
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+ - Ent/precision: 0.6776
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+ - Ent/accuracy: 0.7365
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+ - Ent/f1: 0.7058
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+ - Con/precision: 0.6640
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+ - Con/accuracy: 0.6151
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+ - Con/f1: 0.6386
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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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+ | 0.1899 | 1.0 | 1875 | 0.1927 | 0.6330 | 0.6682 | 0.9163 | 0.6502 | 0.6283 | 0.7398 | 0.6795 | 0.6438 | 0.5513 | 0.5940 |
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+ | 0.137 | 2.0 | 3750 | 0.1988 | 0.6405 | 0.6959 | 0.9160 | 0.6671 | 0.6461 | 0.7475 | 0.6931 | 0.6297 | 0.6116 | 0.6205 |
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+ | 0.101 | 3.0 | 5625 | 0.2375 | 0.6494 | 0.7188 | 0.9173 | 0.6824 | 0.6497 | 0.7743 | 0.7066 | 0.6488 | 0.6281 | 0.6383 |
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+ | 0.0767 | 4.0 | 7500 | 0.2699 | 0.6533 | 0.7188 | 0.9154 | 0.6845 | 0.6575 | 0.7741 | 0.7111 | 0.6449 | 0.6285 | 0.6366 |
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+ | 0.057 | 5.0 | 9375 | 0.3188 | 0.6696 | 0.6914 | 0.9163 | 0.6803 | 0.6790 | 0.7405 | 0.7084 | 0.6518 | 0.6112 | 0.6308 |
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+ | 0.0423 | 6.0 | 11250 | 0.3646 | 0.6773 | 0.6846 | 0.9171 | 0.6809 | 0.6787 | 0.7388 | 0.7075 | 0.6746 | 0.5959 | 0.6328 |
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+ | 0.0339 | 7.0 | 13125 | 0.4007 | 0.6711 | 0.6816 | 0.9151 | 0.6763 | 0.6782 | 0.7283 | 0.7023 | 0.6575 | 0.6055 | 0.6304 |
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+ | 0.0282 | 8.0 | 15000 | 0.4196 | 0.6729 | 0.6904 | 0.9163 | 0.6815 | 0.6776 | 0.7365 | 0.7058 | 0.6640 | 0.6151 | 0.6386 |
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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