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--- |
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license: cc-by-4.0 |
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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: finetuned-ner |
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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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# finetuned-ner |
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This model is a fine-tuned version of [deepset/deberta-v3-base-squad2](https://huggingface.co/deepset/deberta-v3-base-squad2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4783 |
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- Precision: 0.3264 |
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- Recall: 0.3591 |
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- F1: 0.3420 |
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- Accuracy: 0.8925 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.05 |
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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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| 39.8167 | 1.0 | 760 | 0.3957 | 0.1844 | 0.2909 | 0.2257 | 0.8499 | |
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| 21.7333 | 2.0 | 1520 | 0.3853 | 0.2118 | 0.3273 | 0.2571 | 0.8546 | |
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| 13.8859 | 3.0 | 2280 | 0.3631 | 0.2443 | 0.2909 | 0.2656 | 0.8789 | |
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| 20.6586 | 4.0 | 3040 | 0.3961 | 0.2946 | 0.3455 | 0.3180 | 0.8753 | |
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| 13.8654 | 5.0 | 3800 | 0.3821 | 0.2791 | 0.3273 | 0.3013 | 0.8877 | |
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| 12.6942 | 6.0 | 4560 | 0.4393 | 0.3122 | 0.3364 | 0.3239 | 0.8909 | |
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| 25.0549 | 7.0 | 5320 | 0.4542 | 0.3106 | 0.3727 | 0.3388 | 0.8824 | |
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| 5.6816 | 8.0 | 6080 | 0.4432 | 0.2820 | 0.3409 | 0.3086 | 0.8774 | |
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| 13.1296 | 9.0 | 6840 | 0.4509 | 0.2884 | 0.35 | 0.3162 | 0.8824 | |
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| 7.7173 | 10.0 | 7600 | 0.4265 | 0.3170 | 0.3818 | 0.3464 | 0.8919 | |
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| 6.7922 | 11.0 | 8360 | 0.4749 | 0.3320 | 0.3818 | 0.3552 | 0.8892 | |
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| 5.4287 | 12.0 | 9120 | 0.4564 | 0.2917 | 0.3818 | 0.3307 | 0.8805 | |
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| 7.4153 | 13.0 | 9880 | 0.4735 | 0.2963 | 0.3273 | 0.3110 | 0.8871 | |
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| 9.1154 | 14.0 | 10640 | 0.4553 | 0.3416 | 0.3773 | 0.3585 | 0.8894 | |
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| 5.999 | 15.0 | 11400 | 0.4489 | 0.3203 | 0.4091 | 0.3593 | 0.8880 | |
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| 9.5128 | 16.0 | 12160 | 0.4947 | 0.3164 | 0.3682 | 0.3403 | 0.8883 | |
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| 5.6713 | 17.0 | 12920 | 0.4705 | 0.3527 | 0.3864 | 0.3688 | 0.8919 | |
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| 12.2119 | 18.0 | 13680 | 0.4617 | 0.3123 | 0.3591 | 0.3340 | 0.8857 | |
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| 8.5658 | 19.0 | 14440 | 0.4764 | 0.3092 | 0.35 | 0.3284 | 0.8944 | |
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| 11.0664 | 20.0 | 15200 | 0.4557 | 0.3187 | 0.3636 | 0.3397 | 0.8905 | |
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| 6.7161 | 21.0 | 15960 | 0.4468 | 0.3210 | 0.3955 | 0.3544 | 0.8956 | |
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| 9.0448 | 22.0 | 16720 | 0.5120 | 0.2872 | 0.3682 | 0.3227 | 0.8792 | |
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| 6.573 | 23.0 | 17480 | 0.4990 | 0.3307 | 0.3773 | 0.3524 | 0.8869 | |
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| 5.0543 | 24.0 | 18240 | 0.4763 | 0.3028 | 0.3455 | 0.3227 | 0.8899 | |
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| 6.8797 | 25.0 | 19000 | 0.4814 | 0.2780 | 0.3273 | 0.3006 | 0.8913 | |
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| 7.7544 | 26.0 | 19760 | 0.4695 | 0.3024 | 0.3409 | 0.3205 | 0.8946 | |
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| 4.8346 | 27.0 | 20520 | 0.4849 | 0.3154 | 0.3455 | 0.3297 | 0.8931 | |
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| 4.4766 | 28.0 | 21280 | 0.4809 | 0.2925 | 0.3364 | 0.3129 | 0.8913 | |
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| 7.9149 | 29.0 | 22040 | 0.4756 | 0.3238 | 0.3591 | 0.3405 | 0.8930 | |
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| 7.3033 | 30.0 | 22800 | 0.4783 | 0.3264 | 0.3591 | 0.3420 | 0.8925 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.7.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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