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Tommert25
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multibert_dataaugmentation

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +19 -19
  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 [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6354
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- - Precisions: 0.8725
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- - Recall: 0.8025
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- - F-measure: 0.8295
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- - Accuracy: 0.8996
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  ## Model description
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@@ -53,20 +53,20 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
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- | 0.5999 | 1.0 | 284 | 0.4542 | 0.8507 | 0.7167 | 0.7422 | 0.8708 |
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- | 0.2808 | 2.0 | 568 | 0.3886 | 0.8552 | 0.7964 | 0.8192 | 0.8864 |
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- | 0.1596 | 3.0 | 852 | 0.4749 | 0.8786 | 0.7893 | 0.8208 | 0.8905 |
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- | 0.1035 | 4.0 | 1136 | 0.5060 | 0.8568 | 0.8034 | 0.8208 | 0.8954 |
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- | 0.0719 | 5.0 | 1420 | 0.5716 | 0.8498 | 0.8102 | 0.8255 | 0.8919 |
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- | 0.0456 | 6.0 | 1704 | 0.6255 | 0.8701 | 0.8060 | 0.8279 | 0.8960 |
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- | 0.0284 | 7.0 | 1988 | 0.6354 | 0.8725 | 0.8025 | 0.8295 | 0.8996 |
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- | 0.0205 | 8.0 | 2272 | 0.7146 | 0.8518 | 0.8105 | 0.8266 | 0.8988 |
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- | 0.011 | 9.0 | 2556 | 0.7307 | 0.8614 | 0.8045 | 0.8279 | 0.9004 |
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- | 0.0082 | 10.0 | 2840 | 0.7403 | 0.8785 | 0.7988 | 0.8255 | 0.9009 |
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- | 0.0064 | 11.0 | 3124 | 0.7756 | 0.8809 | 0.7913 | 0.8217 | 0.8989 |
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- | 0.0039 | 12.0 | 3408 | 0.8036 | 0.8650 | 0.7877 | 0.8130 | 0.8966 |
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- | 0.0038 | 13.0 | 3692 | 0.7660 | 0.8781 | 0.7950 | 0.8222 | 0.8997 |
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- | 0.0025 | 14.0 | 3976 | 0.7640 | 0.8829 | 0.7961 | 0.8249 | 0.9012 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7138
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+ - Precisions: 0.8609
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+ - Recall: 0.8356
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+ - F-measure: 0.8464
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+ - Accuracy: 0.8989
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
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+ | 0.5775 | 1.0 | 285 | 0.4827 | 0.7847 | 0.7040 | 0.7340 | 0.8509 |
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+ | 0.2623 | 2.0 | 570 | 0.5829 | 0.8035 | 0.7359 | 0.7591 | 0.8613 |
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+ | 0.1503 | 3.0 | 855 | 0.5609 | 0.7946 | 0.8083 | 0.7917 | 0.8804 |
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+ | 0.088 | 4.0 | 1140 | 0.5481 | 0.8406 | 0.7997 | 0.8170 | 0.8860 |
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+ | 0.0592 | 5.0 | 1425 | 0.6359 | 0.8207 | 0.8210 | 0.8120 | 0.8828 |
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+ | 0.0414 | 6.0 | 1710 | 0.6589 | 0.8313 | 0.8171 | 0.8198 | 0.8843 |
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+ | 0.0271 | 7.0 | 1995 | 0.7117 | 0.8689 | 0.7882 | 0.8216 | 0.8936 |
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+ | 0.0179 | 8.0 | 2280 | 0.7138 | 0.8609 | 0.8356 | 0.8464 | 0.8989 |
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+ | 0.0121 | 9.0 | 2565 | 0.7289 | 0.8456 | 0.8128 | 0.8278 | 0.8946 |
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+ | 0.0081 | 10.0 | 2850 | 0.7603 | 0.8344 | 0.8223 | 0.8278 | 0.8956 |
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+ | 0.0058 | 11.0 | 3135 | 0.8126 | 0.8576 | 0.8107 | 0.8322 | 0.8942 |
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+ | 0.0041 | 12.0 | 3420 | 0.8004 | 0.8582 | 0.8267 | 0.8415 | 0.8955 |
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+ | 0.0031 | 13.0 | 3705 | 0.7936 | 0.8599 | 0.8275 | 0.8426 | 0.8961 |
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+ | 0.0028 | 14.0 | 3990 | 0.8076 | 0.8602 | 0.8226 | 0.8401 | 0.8966 |
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  ### Framework versions
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