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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: bert-base-german-cased-amdi-synset |
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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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# bert-base-german-cased-amdi-synset |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5931 |
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- Accuracy: 0.8296 |
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- F1: 0.6552 |
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- Precision: 0.6671 |
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- Recall: 0.6838 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 3.8594 | 0.3049 | 50 | 3.1098 | 0.3546 | 0.1123 | 0.1516 | 0.1536 | |
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| 2.0232 | 0.6098 | 100 | 1.3093 | 0.6799 | 0.3477 | 0.3439 | 0.4111 | |
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| 1.2061 | 0.9146 | 150 | 1.0550 | 0.7005 | 0.4147 | 0.4034 | 0.4774 | |
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| 0.8326 | 1.2195 | 200 | 0.8302 | 0.7504 | 0.4769 | 0.5008 | 0.5181 | |
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| 0.7385 | 1.5244 | 250 | 0.7518 | 0.7659 | 0.5069 | 0.5359 | 0.5543 | |
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| 0.6504 | 1.8293 | 300 | 0.7083 | 0.7762 | 0.5155 | 0.4996 | 0.5648 | |
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| 0.6269 | 2.1341 | 350 | 0.6032 | 0.8176 | 0.5909 | 0.5914 | 0.6244 | |
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| 0.4735 | 2.4390 | 400 | 0.6070 | 0.8090 | 0.6165 | 0.6480 | 0.6377 | |
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| 0.4269 | 2.7439 | 450 | 0.6315 | 0.8090 | 0.6380 | 0.6571 | 0.6666 | |
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| 0.4783 | 3.0488 | 500 | 0.5931 | 0.8296 | 0.6552 | 0.6671 | 0.6838 | |
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| 0.3407 | 3.3537 | 550 | 0.5612 | 0.8382 | 0.6595 | 0.6541 | 0.6934 | |
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| 0.3022 | 3.6585 | 600 | 0.5809 | 0.8262 | 0.6694 | 0.6828 | 0.6933 | |
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| 0.3161 | 3.9634 | 650 | 0.5659 | 0.8434 | 0.6834 | 0.6953 | 0.7053 | |
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| 0.2405 | 4.2683 | 700 | 0.6109 | 0.8382 | 0.6643 | 0.6651 | 0.6965 | |
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| 0.2201 | 4.5732 | 750 | 0.5762 | 0.8485 | 0.6880 | 0.6913 | 0.7115 | |
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| 0.2188 | 4.8780 | 800 | 0.5860 | 0.8485 | 0.6875 | 0.6911 | 0.7129 | |
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| 0.16 | 5.1829 | 850 | 0.6092 | 0.8399 | 0.6630 | 0.6681 | 0.6882 | |
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| 0.1456 | 5.4878 | 900 | 0.6303 | 0.8417 | 0.6646 | 0.6718 | 0.6873 | |
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| 0.1718 | 5.7927 | 950 | 0.6210 | 0.8468 | 0.6703 | 0.6734 | 0.6947 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.20.3 |
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