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bert-base-german-cased-amdi-synset

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kugler/bert-base-german-cased-amdi-synset

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  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.7086
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- - Accuracy: 0.8055
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- - F1: 0.5536
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- - Precision: 0.5654
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- - Recall: 0.5744
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  ## Model description
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@@ -43,147 +43,37 @@ More information needed
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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: 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: 10
 
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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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- | 3.7615 | 0.0765 | 50 | 3.1888 | 0.2874 | 0.0867 | 0.0882 | 0.1287 |
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- | 2.6256 | 0.1529 | 100 | 1.9358 | 0.6299 | 0.2547 | 0.2377 | 0.3115 |
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- | 1.5993 | 0.2294 | 150 | 1.4048 | 0.6678 | 0.2951 | 0.2620 | 0.3522 |
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- | 1.4846 | 0.3058 | 200 | 1.2470 | 0.6644 | 0.3032 | 0.2756 | 0.3724 |
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- | 1.1822 | 0.3823 | 250 | 1.1040 | 0.7143 | 0.3859 | 0.4011 | 0.4329 |
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- | 1.1861 | 0.4587 | 300 | 0.9793 | 0.7315 | 0.4110 | 0.4163 | 0.4500 |
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- | 0.9813 | 0.5352 | 350 | 0.9844 | 0.7177 | 0.3638 | 0.3368 | 0.4152 |
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- | 1.0233 | 0.6116 | 400 | 0.8853 | 0.7315 | 0.3922 | 0.3827 | 0.4400 |
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- | 0.9335 | 0.6881 | 450 | 0.8682 | 0.7229 | 0.4029 | 0.3761 | 0.4555 |
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- | 0.8632 | 0.7645 | 500 | 0.8938 | 0.7074 | 0.3958 | 0.3929 | 0.4395 |
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- | 0.8554 | 0.8410 | 550 | 0.8641 | 0.7349 | 0.4354 | 0.4495 | 0.4842 |
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- | 0.745 | 0.9174 | 600 | 0.7404 | 0.7694 | 0.4471 | 0.4487 | 0.4852 |
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- | 0.8285 | 0.9939 | 650 | 0.7802 | 0.7556 | 0.4373 | 0.4304 | 0.4871 |
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- | 0.6485 | 1.0703 | 700 | 0.7980 | 0.7522 | 0.4645 | 0.5001 | 0.5074 |
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- | 0.7505 | 1.1468 | 750 | 0.7127 | 0.7556 | 0.4711 | 0.4691 | 0.5112 |
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- | 0.5805 | 1.2232 | 800 | 0.7690 | 0.7762 | 0.4965 | 0.5064 | 0.5126 |
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- | 0.6321 | 1.2997 | 850 | 0.7761 | 0.7625 | 0.4863 | 0.4991 | 0.5119 |
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- | 0.6786 | 1.3761 | 900 | 0.7174 | 0.7952 | 0.4955 | 0.5092 | 0.5159 |
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- | 0.6474 | 1.4526 | 950 | 0.7714 | 0.7762 | 0.4972 | 0.4989 | 0.5204 |
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- | 0.6404 | 1.5291 | 1000 | 0.7914 | 0.7539 | 0.4575 | 0.4634 | 0.4901 |
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- | 0.593 | 1.6055 | 1050 | 0.7073 | 0.7780 | 0.5103 | 0.5142 | 0.5448 |
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- | 0.5835 | 1.6820 | 1100 | 0.7399 | 0.7866 | 0.5209 | 0.5177 | 0.5573 |
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- | 0.6412 | 1.7584 | 1150 | 0.6477 | 0.8124 | 0.5439 | 0.5451 | 0.5699 |
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- | 0.4541 | 1.8349 | 1200 | 0.7352 | 0.7780 | 0.5149 | 0.5318 | 0.5468 |
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- | 0.7073 | 1.9113 | 1250 | 0.6252 | 0.8003 | 0.5313 | 0.5134 | 0.5742 |
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- | 0.5594 | 1.9878 | 1300 | 0.6769 | 0.7935 | 0.5535 | 0.5738 | 0.5820 |
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- | 0.5304 | 2.0642 | 1350 | 0.6631 | 0.8124 | 0.5716 | 0.5704 | 0.6021 |
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- | 0.3383 | 2.1407 | 1400 | 0.6642 | 0.8021 | 0.5527 | 0.5709 | 0.5776 |
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- | 0.3957 | 2.2171 | 1450 | 0.6631 | 0.8193 | 0.5803 | 0.5811 | 0.5950 |
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- | 0.3652 | 2.2936 | 1500 | 0.7086 | 0.8055 | 0.5536 | 0.5654 | 0.5744 |
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- | 0.4445 | 2.3700 | 1550 | 0.7142 | 0.8003 | 0.5558 | 0.5605 | 0.5908 |
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- | 0.411 | 2.4465 | 1600 | 0.7134 | 0.8072 | 0.5644 | 0.5798 | 0.5742 |
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- | 0.5233 | 2.5229 | 1650 | 0.6846 | 0.8107 | 0.5466 | 0.5523 | 0.5646 |
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- | 0.407 | 2.5994 | 1700 | 0.6663 | 0.8158 | 0.5586 | 0.5669 | 0.5740 |
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- | 0.378 | 2.6758 | 1750 | 0.7391 | 0.8124 | 0.5642 | 0.5787 | 0.5854 |
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- | 0.3595 | 2.7523 | 1800 | 0.7391 | 0.8176 | 0.5747 | 0.5935 | 0.5896 |
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- | 0.4271 | 2.8287 | 1850 | 0.7237 | 0.8176 | 0.5753 | 0.5796 | 0.5980 |
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- | 0.3514 | 2.9052 | 1900 | 0.7601 | 0.8244 | 0.5717 | 0.5853 | 0.5849 |
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- | 0.4118 | 2.9817 | 1950 | 0.6944 | 0.8176 | 0.5809 | 0.5895 | 0.5986 |
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- | 0.3361 | 3.0581 | 2000 | 0.7427 | 0.8176 | 0.5828 | 0.5853 | 0.6047 |
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- | 0.2835 | 3.1346 | 2050 | 0.7423 | 0.8210 | 0.5698 | 0.5776 | 0.5826 |
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- | 0.2791 | 3.2110 | 2100 | 0.8072 | 0.8090 | 0.5544 | 0.5630 | 0.5710 |
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- | 0.3093 | 3.2875 | 2150 | 0.8117 | 0.8158 | 0.5648 | 0.5690 | 0.5838 |
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- | 0.2683 | 3.3639 | 2200 | 0.7990 | 0.8296 | 0.5710 | 0.5679 | 0.5854 |
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- | 0.2549 | 3.4404 | 2250 | 0.8160 | 0.8348 | 0.5872 | 0.5914 | 0.6003 |
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- | 0.2647 | 3.5168 | 2300 | 0.8324 | 0.8330 | 0.5925 | 0.5943 | 0.6044 |
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- | 0.2367 | 3.5933 | 2350 | 0.8226 | 0.8227 | 0.5946 | 0.5961 | 0.6100 |
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- | 0.2644 | 3.6697 | 2400 | 0.9041 | 0.8193 | 0.5908 | 0.5995 | 0.6083 |
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- | 0.3544 | 3.7462 | 2450 | 0.9154 | 0.8141 | 0.5825 | 0.5962 | 0.6005 |
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- | 0.2654 | 3.8226 | 2500 | 0.8085 | 0.8210 | 0.6050 | 0.6159 | 0.6208 |
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- | 0.2305 | 3.8991 | 2550 | 0.8421 | 0.8262 | 0.5759 | 0.5798 | 0.5917 |
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- | 0.3305 | 3.9755 | 2600 | 0.8312 | 0.8193 | 0.6068 | 0.6288 | 0.6139 |
109
- | 0.2711 | 4.0520 | 2650 | 0.8650 | 0.8210 | 0.5859 | 0.5939 | 0.5924 |
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- | 0.137 | 4.1284 | 2700 | 0.8813 | 0.8227 | 0.5809 | 0.5876 | 0.5966 |
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- | 0.2072 | 4.2049 | 2750 | 0.8558 | 0.8348 | 0.6091 | 0.6353 | 0.6184 |
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- | 0.226 | 4.2813 | 2800 | 0.8628 | 0.8296 | 0.5861 | 0.5835 | 0.6021 |
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- | 0.153 | 4.3578 | 2850 | 0.8712 | 0.8382 | 0.6062 | 0.6157 | 0.6110 |
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- | 0.125 | 4.4343 | 2900 | 0.8996 | 0.8417 | 0.6179 | 0.6234 | 0.6282 |
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- | 0.1186 | 4.5107 | 2950 | 0.8958 | 0.8382 | 0.6098 | 0.6103 | 0.6198 |
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- | 0.1498 | 4.5872 | 3000 | 0.9907 | 0.8072 | 0.5869 | 0.5980 | 0.6086 |
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- | 0.1228 | 4.6636 | 3050 | 0.9276 | 0.8244 | 0.6113 | 0.6243 | 0.6234 |
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- | 0.0896 | 4.7401 | 3100 | 0.8962 | 0.8348 | 0.6265 | 0.6358 | 0.6379 |
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- | 0.1722 | 4.8165 | 3150 | 0.9404 | 0.8244 | 0.5922 | 0.5897 | 0.6162 |
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- | 0.2493 | 4.8930 | 3200 | 0.9081 | 0.8279 | 0.6016 | 0.5913 | 0.6259 |
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- | 0.1868 | 4.9694 | 3250 | 0.9450 | 0.8330 | 0.6024 | 0.6044 | 0.6191 |
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- | 0.0714 | 5.0459 | 3300 | 0.9498 | 0.8279 | 0.6039 | 0.6125 | 0.6228 |
123
- | 0.0967 | 5.1223 | 3350 | 0.9723 | 0.8244 | 0.6094 | 0.6166 | 0.6265 |
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- | 0.1341 | 5.1988 | 3400 | 0.9906 | 0.8227 | 0.5975 | 0.6125 | 0.6120 |
125
- | 0.1067 | 5.2752 | 3450 | 0.9759 | 0.8313 | 0.5980 | 0.6042 | 0.6169 |
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- | 0.05 | 5.3517 | 3500 | 0.9823 | 0.8296 | 0.5967 | 0.6034 | 0.6133 |
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- | 0.0561 | 5.4281 | 3550 | 1.0020 | 0.8313 | 0.6041 | 0.6098 | 0.6224 |
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- | 0.0716 | 5.5046 | 3600 | 1.0264 | 0.8279 | 0.6013 | 0.6107 | 0.6113 |
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- | 0.0476 | 5.5810 | 3650 | 1.0426 | 0.8296 | 0.6125 | 0.6216 | 0.6277 |
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- | 0.135 | 5.6575 | 3700 | 1.0367 | 0.8227 | 0.6401 | 0.6618 | 0.6490 |
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- | 0.0948 | 5.7339 | 3750 | 0.9911 | 0.8399 | 0.6507 | 0.6599 | 0.6557 |
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- | 0.0306 | 5.8104 | 3800 | 0.9729 | 0.8382 | 0.6400 | 0.6468 | 0.6495 |
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- | 0.1203 | 5.8869 | 3850 | 1.0240 | 0.8244 | 0.6230 | 0.6453 | 0.6272 |
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- | 0.1401 | 5.9633 | 3900 | 1.0078 | 0.8279 | 0.6191 | 0.6400 | 0.6251 |
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- | 0.0875 | 6.0398 | 3950 | 1.0308 | 0.8279 | 0.6023 | 0.6201 | 0.6089 |
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- | 0.0568 | 6.1162 | 4000 | 0.9964 | 0.8262 | 0.5989 | 0.6137 | 0.6049 |
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- | 0.0364 | 6.1927 | 4050 | 0.9775 | 0.8313 | 0.6067 | 0.6301 | 0.6126 |
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- | 0.0391 | 6.2691 | 4100 | 1.0063 | 0.8313 | 0.6323 | 0.6539 | 0.6364 |
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- | 0.0327 | 6.3456 | 4150 | 1.0221 | 0.8227 | 0.6136 | 0.6231 | 0.6175 |
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- | 0.0587 | 6.4220 | 4200 | 1.0493 | 0.8262 | 0.6308 | 0.6408 | 0.6347 |
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- | 0.0421 | 6.4985 | 4250 | 1.0646 | 0.8296 | 0.6228 | 0.6367 | 0.6281 |
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- | 0.0397 | 6.5749 | 4300 | 1.0177 | 0.8262 | 0.6156 | 0.6359 | 0.6211 |
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- | 0.0425 | 6.6514 | 4350 | 1.0295 | 0.8296 | 0.6239 | 0.6360 | 0.6286 |
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- | 0.0206 | 6.7278 | 4400 | 1.0322 | 0.8348 | 0.6261 | 0.6485 | 0.6294 |
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- | 0.0438 | 6.8043 | 4450 | 1.0312 | 0.8296 | 0.6076 | 0.6173 | 0.6115 |
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- | 0.0515 | 6.8807 | 4500 | 1.0735 | 0.8193 | 0.6137 | 0.6150 | 0.6249 |
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- | 0.0768 | 6.9572 | 4550 | 1.1468 | 0.8193 | 0.6056 | 0.6143 | 0.6133 |
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- | 0.0427 | 7.0336 | 4600 | 1.0917 | 0.8193 | 0.6005 | 0.6127 | 0.6042 |
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- | 0.0254 | 7.1101 | 4650 | 1.1042 | 0.8210 | 0.6026 | 0.6165 | 0.6035 |
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- | 0.006 | 7.1865 | 4700 | 1.1204 | 0.8176 | 0.5793 | 0.5914 | 0.5826 |
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- | 0.0321 | 7.2630 | 4750 | 1.1171 | 0.8227 | 0.5957 | 0.6048 | 0.5993 |
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- | 0.0292 | 7.3394 | 4800 | 1.1265 | 0.8141 | 0.5930 | 0.6047 | 0.5946 |
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- | 0.0158 | 7.4159 | 4850 | 1.1176 | 0.8193 | 0.6040 | 0.6164 | 0.6046 |
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- | 0.0273 | 7.4924 | 4900 | 1.1194 | 0.8210 | 0.6052 | 0.6146 | 0.6094 |
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- | 0.0467 | 7.5688 | 4950 | 1.1171 | 0.8176 | 0.6121 | 0.6222 | 0.6158 |
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- | 0.0155 | 7.6453 | 5000 | 1.1207 | 0.8227 | 0.6067 | 0.6196 | 0.6085 |
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- | 0.0158 | 7.7217 | 5050 | 1.1188 | 0.8262 | 0.6216 | 0.6394 | 0.6212 |
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- | 0.0383 | 7.7982 | 5100 | 1.0977 | 0.8330 | 0.6242 | 0.6412 | 0.6250 |
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- | 0.0535 | 7.8746 | 5150 | 1.1085 | 0.8330 | 0.6249 | 0.6431 | 0.6262 |
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- | 0.0478 | 7.9511 | 5200 | 1.1164 | 0.8382 | 0.6470 | 0.6651 | 0.6481 |
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- | 0.0275 | 8.0275 | 5250 | 1.1208 | 0.8399 | 0.6598 | 0.6765 | 0.6630 |
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- | 0.0076 | 8.1040 | 5300 | 1.1152 | 0.8365 | 0.6416 | 0.6574 | 0.6416 |
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- | 0.0153 | 8.1804 | 5350 | 1.1129 | 0.8348 | 0.6395 | 0.6562 | 0.6390 |
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- | 0.0077 | 8.2569 | 5400 | 1.1243 | 0.8330 | 0.6431 | 0.6620 | 0.6417 |
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- | 0.0116 | 8.3333 | 5450 | 1.1258 | 0.8330 | 0.6393 | 0.6593 | 0.6382 |
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- | 0.0083 | 8.4098 | 5500 | 1.1212 | 0.8330 | 0.6302 | 0.6490 | 0.6307 |
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- | 0.0291 | 8.4862 | 5550 | 1.1340 | 0.8330 | 0.6405 | 0.6600 | 0.6411 |
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- | 0.0202 | 8.5627 | 5600 | 1.1388 | 0.8348 | 0.6423 | 0.6616 | 0.6426 |
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- | 0.0333 | 8.6391 | 5650 | 1.1452 | 0.8313 | 0.6299 | 0.6484 | 0.6300 |
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- | 0.0045 | 8.7156 | 5700 | 1.1460 | 0.8382 | 0.6552 | 0.6739 | 0.6544 |
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- | 0.0201 | 8.7920 | 5750 | 1.1466 | 0.8365 | 0.6495 | 0.6676 | 0.6492 |
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- | 0.0133 | 8.8685 | 5800 | 1.1461 | 0.8348 | 0.6474 | 0.6664 | 0.6465 |
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- | 0.0317 | 8.9450 | 5850 | 1.1530 | 0.8348 | 0.6430 | 0.6614 | 0.6435 |
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- | 0.0065 | 9.0214 | 5900 | 1.1490 | 0.8313 | 0.6400 | 0.6588 | 0.6404 |
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- | 0.0035 | 9.0979 | 5950 | 1.1545 | 0.8313 | 0.6443 | 0.6646 | 0.6435 |
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- | 0.0104 | 9.1743 | 6000 | 1.1619 | 0.8313 | 0.6459 | 0.6654 | 0.6454 |
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- | 0.0109 | 9.2508 | 6050 | 1.1603 | 0.8313 | 0.6459 | 0.6654 | 0.6454 |
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- | 0.0028 | 9.3272 | 6100 | 1.1563 | 0.8330 | 0.6474 | 0.6668 | 0.6471 |
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- | 0.0157 | 9.4037 | 6150 | 1.1553 | 0.8348 | 0.6480 | 0.6672 | 0.6478 |
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- | 0.0072 | 9.4801 | 6200 | 1.1530 | 0.8313 | 0.6417 | 0.6565 | 0.6433 |
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- | 0.0064 | 9.5566 | 6250 | 1.1572 | 0.8296 | 0.6401 | 0.6552 | 0.6416 |
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- | 0.0183 | 9.6330 | 6300 | 1.1586 | 0.8296 | 0.6401 | 0.6552 | 0.6416 |
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- | 0.0221 | 9.7095 | 6350 | 1.1592 | 0.8296 | 0.6401 | 0.6552 | 0.6416 |
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- | 0.0129 | 9.7859 | 6400 | 1.1599 | 0.8296 | 0.6401 | 0.6552 | 0.6416 |
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- | 0.014 | 9.8624 | 6450 | 1.1590 | 0.8296 | 0.6401 | 0.6552 | 0.6416 |
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- | 0.0019 | 9.9388 | 6500 | 1.1596 | 0.8296 | 0.6401 | 0.6552 | 0.6416 |
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  ### Framework versions
 
19
 
20
  This model was trained from scratch on the None dataset.
21
  It achieves the following results on the evaluation set:
22
+ - Loss: 0.5931
23
+ - Accuracy: 0.8296
24
+ - F1: 0.6552
25
+ - Precision: 0.6671
26
+ - Recall: 0.6838
27
 
28
  ## Model description
29
 
 
43
 
44
  The following hyperparameters were used during training:
45
  - learning_rate: 5e-05
46
+ - train_batch_size: 32
47
+ - eval_batch_size: 32
48
  - seed: 42
49
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
  - lr_scheduler_type: linear
51
+ - lr_scheduler_warmup_steps: 50
52
+ - num_epochs: 6
53
 
54
  ### Training results
55
 
56
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
57
  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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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 |
61
+ | 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 |
65
+ | 0.4735 | 2.4390 | 400 | 0.6070 | 0.8090 | 0.6165 | 0.6480 | 0.6377 |
66
+ | 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 |
71
+ | 0.2405 | 4.2683 | 700 | 0.6109 | 0.8382 | 0.6643 | 0.6651 | 0.6965 |
72
+ | 0.2201 | 4.5732 | 750 | 0.5762 | 0.8485 | 0.6880 | 0.6913 | 0.7115 |
73
+ | 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 |
75
+ | 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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