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Aleksandar
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bert-srb-ner-setimes

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
Aleksandar commited on
Commit
e4c4530
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1 Parent(s): 417b805

add model

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Files changed (3) hide show
  1. README.md +12 -8
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -17,7 +17,7 @@ model_index:
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  metric:
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  name: Accuracy
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  type: accuracy
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- value: 0.9216613202768867
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -27,11 +27,11 @@ 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: 0.2799
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- - Precision: 0.6112
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- - Recall: 0.6260
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- - F1: 0.6185
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- - Accuracy: 0.9217
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  ## Model description
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@@ -56,13 +56,17 @@ 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: 1
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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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- | No log | 1.0 | 104 | 0.2799 | 0.6112 | 0.6260 | 0.6185 | 0.9217 |
 
 
 
 
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  ### Framework versions
 
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  metric:
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  name: Accuracy
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  type: accuracy
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+ value: 0.9546851257808543
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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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: 0.1513
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+ - Precision: 0.7592
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+ - Recall: 0.7956
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+ - F1: 0.7770
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+ - Accuracy: 0.9547
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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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+ | No log | 1.0 | 104 | 0.2352 | 0.6559 | 0.6919 | 0.6734 | 0.9332 |
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+ | No log | 2.0 | 208 | 0.1869 | 0.7063 | 0.7477 | 0.7264 | 0.9448 |
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+ | No log | 3.0 | 312 | 0.1647 | 0.7404 | 0.7767 | 0.7581 | 0.9507 |
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+ | No log | 4.0 | 416 | 0.1548 | 0.7557 | 0.7902 | 0.7726 | 0.9540 |
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+ | 0.2033 | 5.0 | 520 | 0.1513 | 0.7592 | 0.7956 | 0.7770 | 0.9547 |
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  ### Framework versions
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