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Aleksandar
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bert-srb-ner

Token Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
1
Aleksandar commited on
Commit
10902c9
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1 Parent(s): 8f411cd

add model

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Files changed (3) hide show
  1. README.md +17 -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: 0.9334458889076481
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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: 0.2321
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- - Precision: 0.6593
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- - Recall: 0.6877
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- - F1: 0.6732
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- - Accuracy: 0.9334
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  ## Model description
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@@ -56,13 +56,22 @@ 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 | 207 | 0.2321 | 0.6593 | 0.6877 | 0.6732 | 0.9334 |
 
 
 
 
 
 
 
 
 
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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: 0.9641060273510046
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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: 0.1647
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+ - Precision: 0.8247
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+ - Recall: 0.8454
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+ - F1: 0.8349
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+ - Accuracy: 0.9641
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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: 10
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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 | 207 | 0.2040 | 0.7006 | 0.7466 | 0.7228 | 0.9411 |
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+ | No log | 2.0 | 414 | 0.1561 | 0.7299 | 0.7868 | 0.7573 | 0.9519 |
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+ | 0.2313 | 3.0 | 621 | 0.1455 | 0.7693 | 0.7992 | 0.7840 | 0.9567 |
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+ | 0.2313 | 4.0 | 828 | 0.1628 | 0.7760 | 0.8037 | 0.7896 | 0.9570 |
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+ | 0.0828 | 5.0 | 1035 | 0.1424 | 0.7997 | 0.8299 | 0.8145 | 0.9604 |
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+ | 0.0828 | 6.0 | 1242 | 0.1512 | 0.7983 | 0.8361 | 0.8168 | 0.9618 |
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+ | 0.0828 | 7.0 | 1449 | 0.1587 | 0.8084 | 0.8415 | 0.8246 | 0.9627 |
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+ | 0.0362 | 8.0 | 1656 | 0.1613 | 0.8154 | 0.8358 | 0.8255 | 0.9632 |
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+ | 0.0362 | 9.0 | 1863 | 0.1685 | 0.8211 | 0.8429 | 0.8319 | 0.9632 |
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+ | 0.0174 | 10.0 | 2070 | 0.1647 | 0.8247 | 0.8454 | 0.8349 | 0.9641 |
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  ### Framework versions
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