ju-bezdek commited on
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Publishing model

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+ =======
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.json filter=lfs diff=lfs merge=lfs -text
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+ >>>>>>> Publishing model
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+ .DS_Store
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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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+ datasets:
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+ - ju-bezdek/conll2003-SK-NER
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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: outputs
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: ju-bezdek/conll2003-SK-NER
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+ type: ju-bezdek/conll2003-SK-NER
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+ args: conll2003-SK-NER
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8189727994593682
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+ - name: Recall
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+ type: recall
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+ value: 0.8389581169955002
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+ - name: F1
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+ type: f1
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+ value: 0.8288450029922203
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9526157920337243
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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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+ # outputs
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+
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+ This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the ju-bezdek/conll2003-SK-NER dataset.
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+ It achieves the following results on the evaluation (validation) set:
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+ - Loss: 0.1752
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+ - Precision: 0.8190
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+ - Recall: 0.8390
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+ - F1: 0.8288
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+ - Accuracy: 0.9526
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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: 0.0001
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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: 15
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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 | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3237 | 1.0 | 878 | 0.2541 | 0.7125 | 0.8059 | 0.7563 | 0.9283 |
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+ | 0.1663 | 2.0 | 1756 | 0.2370 | 0.7775 | 0.8090 | 0.7929 | 0.9394 |
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+ | 0.1251 | 3.0 | 2634 | 0.2289 | 0.7732 | 0.8029 | 0.7878 | 0.9385 |
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+ | 0.0984 | 4.0 | 3512 | 0.2818 | 0.7294 | 0.8189 | 0.7715 | 0.9294 |
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+ | 0.0808 | 5.0 | 4390 | 0.3138 | 0.7615 | 0.7900 | 0.7755 | 0.9326 |
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+ | 0.0578 | 6.0 | 5268 | 0.3072 | 0.7548 | 0.8222 | 0.7871 | 0.9370 |
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+ | 0.0481 | 7.0 | 6146 | 0.2778 | 0.7897 | 0.8156 | 0.8025 | 0.9408 |
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+ | 0.0414 | 8.0 | 7024 | 0.3336 | 0.7695 | 0.8201 | 0.7940 | 0.9389 |
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+ | 0.0268 | 9.0 | 7902 | 0.3294 | 0.7868 | 0.8140 | 0.8002 | 0.9409 |
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+ | 0.0204 | 10.0 | 8780 | 0.3693 | 0.7657 | 0.8239 | 0.7938 | 0.9376 |
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+ | 0.016 | 11.0 | 9658 | 0.3816 | 0.7932 | 0.8242 | 0.8084 | 0.9425 |
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+ | 0.0108 | 12.0 | 10536 | 0.3607 | 0.7929 | 0.8256 | 0.8089 | 0.9431 |
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+ | 0.0078 | 13.0 | 11414 | 0.3980 | 0.7915 | 0.8240 | 0.8074 | 0.9423 |
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+ | 0.0062 | 14.0 | 12292 | 0.4096 | 0.7995 | 0.8247 | 0.8119 | 0.9436 |
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+ | 0.0035 | 15.0 | 13170 | 0.4177 | 0.8006 | 0.8251 | 0.8127 | 0.9438 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.15.0
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+ - Pytorch 1.10.1+cu102
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+ - Datasets 1.17.0
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+ - Tokenizers 0.10.3
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