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End of training

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  1. README.md +22 -14
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -18,11 +18,11 @@ 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-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3288
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- - Precisions: 0.8075
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- - Recall: 0.7510
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- - F-measure: 0.7624
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- - Accuracy: 0.8998
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  ## Model description
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@@ -47,20 +47,28 @@ The following hyperparameters were used during training:
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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: 8
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
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- | No log | 1.0 | 236 | 0.3876 | 0.8471 | 0.6823 | 0.6916 | 0.8775 |
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- | No log | 2.0 | 472 | 0.3288 | 0.8075 | 0.7510 | 0.7624 | 0.8998 |
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- | 0.4702 | 3.0 | 708 | 0.3771 | 0.7893 | 0.7610 | 0.7689 | 0.9031 |
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- | 0.4702 | 4.0 | 944 | 0.4066 | 0.8057 | 0.7695 | 0.7808 | 0.9056 |
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- | 0.1484 | 5.0 | 1180 | 0.4444 | 0.7936 | 0.7783 | 0.7846 | 0.9093 |
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- | 0.1484 | 6.0 | 1416 | 0.4649 | 0.8234 | 0.8018 | 0.8092 | 0.9155 |
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- | 0.0534 | 7.0 | 1652 | 0.4812 | 0.8001 | 0.7877 | 0.7932 | 0.9135 |
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- | 0.0534 | 8.0 | 1888 | 0.4878 | 0.8097 | 0.7894 | 0.7978 | 0.9155 |
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5355
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+ - Precisions: 0.8523
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+ - Recall: 0.8173
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+ - F-measure: 0.8307
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+ - Accuracy: 0.9209
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  ## Model description
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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: 16
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
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+ | 0.6127 | 1.0 | 236 | 0.3656 | 0.8687 | 0.6888 | 0.7011 | 0.8817 |
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+ | 0.3078 | 2.0 | 472 | 0.3390 | 0.8253 | 0.7452 | 0.7612 | 0.8947 |
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+ | 0.1742 | 3.0 | 708 | 0.3899 | 0.7602 | 0.7560 | 0.7469 | 0.8957 |
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+ | 0.1242 | 4.0 | 944 | 0.4402 | 0.8560 | 0.7678 | 0.7861 | 0.9055 |
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+ | 0.0749 | 5.0 | 1180 | 0.4206 | 0.8163 | 0.8139 | 0.8127 | 0.9121 |
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+ | 0.0533 | 6.0 | 1416 | 0.4824 | 0.8257 | 0.7936 | 0.8060 | 0.9124 |
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+ | 0.0366 | 7.0 | 1652 | 0.4927 | 0.8506 | 0.7956 | 0.8158 | 0.9176 |
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+ | 0.0273 | 8.0 | 1888 | 0.5638 | 0.8631 | 0.7855 | 0.8093 | 0.9202 |
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+ | 0.0206 | 9.0 | 2124 | 0.5507 | 0.8322 | 0.7957 | 0.8096 | 0.9141 |
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+ | 0.0154 | 10.0 | 2360 | 0.5355 | 0.8523 | 0.8173 | 0.8307 | 0.9209 |
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+ | 0.0105 | 11.0 | 2596 | 0.5812 | 0.8301 | 0.7961 | 0.8088 | 0.9162 |
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+ | 0.0086 | 12.0 | 2832 | 0.6084 | 0.8357 | 0.8065 | 0.8192 | 0.9130 |
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+ | 0.0046 | 13.0 | 3068 | 0.6035 | 0.8310 | 0.7948 | 0.8104 | 0.9137 |
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+ | 0.0036 | 14.0 | 3304 | 0.6034 | 0.8223 | 0.7980 | 0.8074 | 0.9134 |
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+ | 0.0043 | 15.0 | 3540 | 0.6146 | 0.8198 | 0.7869 | 0.7999 | 0.9120 |
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+ | 0.0018 | 16.0 | 3776 | 0.6070 | 0.8244 | 0.7894 | 0.8029 | 0.9134 |
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  ### Framework versions
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