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denizspynk
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req_mod_ner_modelv2

Token Classification
Transformers
PyTorch
Dutch
roberta
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update model card README.md

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@@ -19,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9225
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- - Precision: 0.5856
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- - Recall: 0.5603
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- - F1: 0.5727
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- - Accuracy: 0.9169
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  ## Model description
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@@ -43,8 +43,8 @@ More information needed
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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: 1
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- - eval_batch_size: 1
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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
@@ -54,38 +54,38 @@ The following hyperparameters were used during training:
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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 | 240 | 0.5029 | 0.5965 | 0.2931 | 0.3931 | 0.8967 |
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- | No log | 2.0 | 480 | 0.4749 | 0.4213 | 0.6466 | 0.5102 | 0.8918 |
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- | 0.4196 | 3.0 | 720 | 0.4722 | 0.525 | 0.5431 | 0.5339 | 0.9072 |
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- | 0.4196 | 4.0 | 960 | 0.4943 | 0.4830 | 0.6121 | 0.5399 | 0.9120 |
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- | 0.1669 | 5.0 | 1200 | 0.5888 | 0.4631 | 0.5948 | 0.5208 | 0.9056 |
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- | 0.1669 | 6.0 | 1440 | 0.4426 | 0.5455 | 0.5172 | 0.5310 | 0.9161 |
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- | 0.1053 | 7.0 | 1680 | 0.6455 | 0.4921 | 0.5345 | 0.5124 | 0.9072 |
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- | 0.1053 | 8.0 | 1920 | 0.7699 | 0.4698 | 0.6034 | 0.5283 | 0.8975 |
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- | 0.0598 | 9.0 | 2160 | 0.7978 | 0.5037 | 0.5862 | 0.5418 | 0.9048 |
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- | 0.0598 | 10.0 | 2400 | 0.7454 | 0.5755 | 0.5259 | 0.5495 | 0.9128 |
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- | 0.0418 | 11.0 | 2640 | 0.6944 | 0.5625 | 0.6207 | 0.5902 | 0.9169 |
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- | 0.0418 | 12.0 | 2880 | 0.7447 | 0.5170 | 0.6552 | 0.5779 | 0.9112 |
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- | 0.0361 | 13.0 | 3120 | 0.7307 | 0.6373 | 0.5603 | 0.5963 | 0.9153 |
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- | 0.0361 | 14.0 | 3360 | 0.6321 | 0.5546 | 0.5690 | 0.5617 | 0.9209 |
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- | 0.0272 | 15.0 | 3600 | 0.7867 | 0.5188 | 0.5948 | 0.5542 | 0.9169 |
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- | 0.0272 | 16.0 | 3840 | 0.7087 | 0.568 | 0.6121 | 0.5892 | 0.9185 |
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- | 0.024 | 17.0 | 4080 | 0.7275 | 0.5522 | 0.6379 | 0.5920 | 0.9153 |
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- | 0.024 | 18.0 | 4320 | 0.8361 | 0.6 | 0.5948 | 0.5974 | 0.9225 |
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- | 0.024 | 19.0 | 4560 | 0.8915 | 0.5263 | 0.6034 | 0.5622 | 0.9144 |
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- | 0.024 | 20.0 | 4800 | 0.7784 | 0.5877 | 0.5776 | 0.5826 | 0.9209 |
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- | 0.0169 | 21.0 | 5040 | 0.8451 | 0.6034 | 0.6034 | 0.6034 | 0.9201 |
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- | 0.0169 | 22.0 | 5280 | 0.8772 | 0.5929 | 0.5776 | 0.5852 | 0.9201 |
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- | 0.0109 | 23.0 | 5520 | 0.9138 | 0.5981 | 0.5517 | 0.5740 | 0.9185 |
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- | 0.0109 | 24.0 | 5760 | 0.8827 | 0.5893 | 0.5690 | 0.5789 | 0.9169 |
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- | 0.0114 | 25.0 | 6000 | 0.8879 | 0.5872 | 0.5517 | 0.5689 | 0.9161 |
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- | 0.0114 | 26.0 | 6240 | 0.9373 | 0.5888 | 0.5431 | 0.5650 | 0.9153 |
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- | 0.0114 | 27.0 | 6480 | 0.9235 | 0.5909 | 0.5603 | 0.5752 | 0.9185 |
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- | 0.0076 | 28.0 | 6720 | 0.9262 | 0.5909 | 0.5603 | 0.5752 | 0.9169 |
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- | 0.0076 | 29.0 | 6960 | 0.9322 | 0.5856 | 0.5603 | 0.5727 | 0.9177 |
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- | 0.0076 | 30.0 | 7200 | 0.9157 | 0.5804 | 0.5603 | 0.5702 | 0.9161 |
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- | 0.0076 | 31.0 | 7440 | 0.9209 | 0.5856 | 0.5603 | 0.5727 | 0.9169 |
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- | 0.0052 | 32.0 | 7680 | 0.9225 | 0.5856 | 0.5603 | 0.5727 | 0.9169 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-ner](https://huggingface.co/pdelobelle/robbert-v2-dutch-ner) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7795
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+ - Precision: 0.5932
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+ - Recall: 0.6034
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+ - F1: 0.5983
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+ - Accuracy: 0.9193
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  ## Model description
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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: 2
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+ - eval_batch_size: 2
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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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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 120 | 0.5525 | 0.6538 | 0.2931 | 0.4048 | 0.9007 |
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+ | No log | 2.0 | 240 | 0.3596 | 0.5447 | 0.5776 | 0.5607 | 0.9120 |
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+ | No log | 3.0 | 360 | 0.4229 | 0.5495 | 0.4310 | 0.4831 | 0.9031 |
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+ | No log | 4.0 | 480 | 0.4091 | 0.5435 | 0.6466 | 0.5906 | 0.9185 |
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+ | 0.2667 | 5.0 | 600 | 0.5476 | 0.6837 | 0.5776 | 0.6262 | 0.9233 |
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+ | 0.2667 | 6.0 | 720 | 0.4703 | 0.5610 | 0.5948 | 0.5774 | 0.9185 |
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+ | 0.2667 | 7.0 | 840 | 0.5904 | 0.5897 | 0.5948 | 0.5923 | 0.9185 |
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+ | 0.2667 | 8.0 | 960 | 0.6285 | 0.5772 | 0.6121 | 0.5941 | 0.9177 |
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+ | 0.0436 | 9.0 | 1080 | 0.7077 | 0.6095 | 0.5517 | 0.5792 | 0.9153 |
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+ | 0.0436 | 10.0 | 1200 | 0.6974 | 0.5929 | 0.5776 | 0.5852 | 0.9177 |
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+ | 0.0436 | 11.0 | 1320 | 0.6777 | 0.5205 | 0.6552 | 0.5802 | 0.9104 |
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+ | 0.0436 | 12.0 | 1440 | 0.6601 | 0.6174 | 0.6121 | 0.6147 | 0.9201 |
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+ | 0.0216 | 13.0 | 1560 | 0.6536 | 0.5809 | 0.6810 | 0.6270 | 0.9209 |
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+ | 0.0216 | 14.0 | 1680 | 0.7329 | 0.5571 | 0.6724 | 0.6094 | 0.9153 |
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+ | 0.0216 | 15.0 | 1800 | 0.7276 | 0.6809 | 0.5517 | 0.6095 | 0.9201 |
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+ | 0.0216 | 16.0 | 1920 | 0.7243 | 0.6017 | 0.6121 | 0.6068 | 0.9209 |
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+ | 0.0164 | 17.0 | 2040 | 0.6963 | 0.592 | 0.6379 | 0.6141 | 0.9217 |
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+ | 0.0164 | 18.0 | 2160 | 0.7418 | 0.6071 | 0.5862 | 0.5965 | 0.9209 |
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+ | 0.0164 | 19.0 | 2280 | 0.8015 | 0.6667 | 0.5690 | 0.6140 | 0.9241 |
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+ | 0.0164 | 20.0 | 2400 | 0.7075 | 0.5168 | 0.6638 | 0.5811 | 0.9136 |
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+ | 0.0098 | 21.0 | 2520 | 0.7847 | 0.6262 | 0.5776 | 0.6009 | 0.9201 |
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+ | 0.0098 | 22.0 | 2640 | 0.7588 | 0.5812 | 0.5862 | 0.5837 | 0.9177 |
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+ | 0.0098 | 23.0 | 2760 | 0.7439 | 0.5530 | 0.6293 | 0.5887 | 0.9153 |
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+ | 0.0098 | 24.0 | 2880 | 0.7619 | 0.5932 | 0.6034 | 0.5983 | 0.9169 |
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+ | 0.0067 | 25.0 | 3000 | 0.7605 | 0.5948 | 0.5948 | 0.5948 | 0.9201 |
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+ | 0.0067 | 26.0 | 3120 | 0.7635 | 0.5847 | 0.5948 | 0.5897 | 0.9185 |
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+ | 0.0067 | 27.0 | 3240 | 0.7732 | 0.6106 | 0.5948 | 0.6026 | 0.9193 |
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+ | 0.0067 | 28.0 | 3360 | 0.7727 | 0.5897 | 0.5948 | 0.5923 | 0.9185 |
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+ | 0.0067 | 29.0 | 3480 | 0.7748 | 0.6 | 0.5948 | 0.5974 | 0.9201 |
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+ | 0.006 | 30.0 | 3600 | 0.7759 | 0.5932 | 0.6034 | 0.5983 | 0.9193 |
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+ | 0.006 | 31.0 | 3720 | 0.7792 | 0.5932 | 0.6034 | 0.5983 | 0.9193 |
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+ | 0.006 | 32.0 | 3840 | 0.7795 | 0.5932 | 0.6034 | 0.5983 | 0.9193 |
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  ### Framework versions