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german-bert-finetuned-ner

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bert
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+ ---
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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: german-bert-finetuned-ner
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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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+ # german-bert-finetuned-ner
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+
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+ This model was trained from scratch on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0907
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+ - Precision: 0.8143
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+ - Recall: 0.8180
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+ - F1: 0.8161
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+ - Accuracy: 0.9893
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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: 8
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+ - eval_batch_size: 8
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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: 20
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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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+ | No log | 1.0 | 120 | 0.0715 | 0.7092 | 0.8 | 0.7518 | 0.9881 |
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+ | No log | 2.0 | 240 | 0.0935 | 0.6588 | 0.8157 | 0.7289 | 0.9845 |
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+ | No log | 3.0 | 360 | 0.0664 | 0.7303 | 0.7910 | 0.7594 | 0.9885 |
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+ | No log | 4.0 | 480 | 0.0647 | 0.6691 | 0.8225 | 0.7379 | 0.9872 |
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+ | 0.0027 | 5.0 | 600 | 0.0752 | 0.8409 | 0.7955 | 0.8176 | 0.9900 |
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+ | 0.0027 | 6.0 | 720 | 0.0658 | 0.7105 | 0.8382 | 0.7691 | 0.9887 |
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+ | 0.0027 | 7.0 | 840 | 0.0818 | 0.8364 | 0.8045 | 0.8202 | 0.9896 |
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+ | 0.0027 | 8.0 | 960 | 0.0791 | 0.7660 | 0.8315 | 0.7974 | 0.9892 |
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+ | 0.0013 | 9.0 | 1080 | 0.0791 | 0.7730 | 0.8112 | 0.7917 | 0.9893 |
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+ | 0.0013 | 10.0 | 1200 | 0.0809 | 0.8117 | 0.8135 | 0.8126 | 0.9889 |
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+ | 0.0013 | 11.0 | 1320 | 0.0851 | 0.8085 | 0.8157 | 0.8121 | 0.9894 |
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+ | 0.0013 | 12.0 | 1440 | 0.0875 | 0.8361 | 0.8022 | 0.8188 | 0.9894 |
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+ | 0.0004 | 13.0 | 1560 | 0.0892 | 0.8395 | 0.8112 | 0.8251 | 0.9893 |
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+ | 0.0004 | 14.0 | 1680 | 0.0857 | 0.7978 | 0.8337 | 0.8154 | 0.9894 |
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+ | 0.0004 | 15.0 | 1800 | 0.0848 | 0.7931 | 0.8270 | 0.8097 | 0.9895 |
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+ | 0.0004 | 16.0 | 1920 | 0.0867 | 0.8053 | 0.8180 | 0.8116 | 0.9896 |
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+ | 0.0002 | 17.0 | 2040 | 0.0866 | 0.7842 | 0.8247 | 0.8039 | 0.9891 |
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+ | 0.0002 | 18.0 | 2160 | 0.0885 | 0.7952 | 0.8202 | 0.8075 | 0.9893 |
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+ | 0.0002 | 19.0 | 2280 | 0.0895 | 0.7948 | 0.8180 | 0.8062 | 0.9894 |
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+ | 0.0002 | 20.0 | 2400 | 0.0907 | 0.8143 | 0.8180 | 0.8161 | 0.9893 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.2
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1