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Dondog
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roberta-base-ner-test-2

Token Classification
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  1. README.md +11 -9
  2. model.safetensors +1 -1
  3. runs/May26_21-15-16_debf144f0859/events.out.tfevents.1716758117.debf144f0859.34.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0742
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- - Precision: 0.8999
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- - Recall: 0.9142
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- - F1: 0.9070
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- - Accuracy: 0.9776
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  ## Model description
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@@ -50,15 +50,17 @@ 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: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.4131 | 1.0 | 60 | 0.1140 | 0.8104 | 0.8399 | 0.8249 | 0.9630 |
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- | 0.0936 | 2.0 | 120 | 0.0785 | 0.8835 | 0.9020 | 0.8926 | 0.9748 |
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- | 0.0617 | 3.0 | 180 | 0.0742 | 0.8999 | 0.9142 | 0.9070 | 0.9776 |
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0790
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+ - Precision: 0.9189
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+ - Recall: 0.9316
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+ - F1: 0.9252
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+ - Accuracy: 0.9794
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.3764 | 1.0 | 60 | 0.1122 | 0.7718 | 0.8424 | 0.8055 | 0.9604 |
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+ | 0.1021 | 2.0 | 120 | 0.0908 | 0.8087 | 0.8760 | 0.8410 | 0.9679 |
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+ | 0.0698 | 3.0 | 180 | 0.0862 | 0.8342 | 0.8861 | 0.8594 | 0.9703 |
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+ | 0.0476 | 4.0 | 240 | 0.0779 | 0.9056 | 0.9242 | 0.9148 | 0.9784 |
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+ | 0.0337 | 5.0 | 300 | 0.0790 | 0.9189 | 0.9316 | 0.9252 | 0.9794 |
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  ### Framework versions
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