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ramybaly
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ner_conll2003

Token Classification
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bert
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add model

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Files changed (6) hide show
  1. README.md +13 -12
  2. pytorch_model.bin +1 -1
  3. runs/Aug21_02-48-54_ip-172-31-23-147/1629514148.6207771/events.out.tfevents.1629514148.ip-172-31-23-147.29186.1 +3 -0
  4. runs/Aug21_02-48-54_ip-172-31-23-147/events.out.tfevents.1629514148.ip-172-31-23-147.29186.0 +3 -0
  5. runs/Aug21_02-48-54_ip-172-31-23-147/events.out.tfevents.1629514639.ip-172-31-23-147.29186.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model_index:
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  metric:
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  name: Accuracy
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  type: accuracy
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- value: 0.9770630290650372
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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
@@ -32,11 +32,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1491
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- - Precision: 0.9010
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- - Recall: 0.9084
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- - F1: 0.9046
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- - Accuracy: 0.9771
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  ## Model description
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@@ -62,17 +62,18 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.1
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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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- | 0.3108 | 1.0 | 877 | 0.0607 | 0.9184 | 0.9345 | 0.9264 | 0.9831 |
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- | 0.0476 | 2.0 | 1754 | 0.0563 | 0.9362 | 0.9430 | 0.9396 | 0.9852 |
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- | 0.0237 | 3.0 | 2631 | 0.0588 | 0.9419 | 0.9476 | 0.9448 | 0.9866 |
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- | 0.0118 | 4.0 | 3508 | 0.0617 | 0.9444 | 0.9523 | 0.9483 | 0.9873 |
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- | 0.007 | 5.0 | 4385 | 0.0645 | 0.9419 | 0.9505 | 0.9462 | 0.9869 |
 
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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: 0.9767860543216715
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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 is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1395
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+ - Precision: 0.8997
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+ - Recall: 0.9007
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+ - F1: 0.9002
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+ - Accuracy: 0.9768
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  ## Model description
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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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  - lr_scheduler_warmup_ratio: 0.1
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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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+ | 0.393 | 1.0 | 877 | 0.0633 | 0.9189 | 0.9311 | 0.9250 | 0.9825 |
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+ | 0.0576 | 2.0 | 1754 | 0.0549 | 0.9319 | 0.9411 | 0.9365 | 0.9855 |
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+ | 0.0293 | 3.0 | 2631 | 0.0624 | 0.9425 | 0.9453 | 0.9439 | 0.9862 |
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+ | 0.0163 | 4.0 | 3508 | 0.0594 | 0.9441 | 0.9458 | 0.9450 | 0.9864 |
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+ | 0.0099 | 5.0 | 4385 | 0.0673 | 0.9422 | 0.9465 | 0.9443 | 0.9865 |
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+ | 0.0059 | 6.0 | 5262 | 0.0711 | 0.9376 | 0.9483 | 0.9429 | 0.9865 |
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  ### Framework versions
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