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ramybaly
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ner_conll2003

Token Classification
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PyTorch
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bert
generated_from_trainer
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8705927
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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_03-10-46_ip-172-31-23-147/1629515451.4981158/events.out.tfevents.1629515451.ip-172-31-23-147.2956.1 +3 -0
  4. runs/Aug21_03-10-46_ip-172-31-23-147/events.out.tfevents.1629515451.ip-172-31-23-147.2956.0 +3 -0
  5. runs/Aug21_03-10-46_ip-172-31-23-147/events.out.tfevents.1629516067.ip-172-31-23-147.2956.2 +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: 0.9767860543216715
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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
@@ -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: 0.1395
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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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@@ -68,12 +68,13 @@ The following hyperparameters were used during training:
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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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  metric:
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  name: Accuracy
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  type: accuracy
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+ value: 0.9772880710440217
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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: 0.1495
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+ - Precision: 0.8985
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+ - Recall: 0.9130
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+ - F1: 0.9057
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+ - Accuracy: 0.9773
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.423 | 1.0 | 877 | 0.0656 | 0.9158 | 0.9268 | 0.9213 | 0.9818 |
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+ | 0.0575 | 2.0 | 1754 | 0.0574 | 0.9285 | 0.9445 | 0.9364 | 0.9847 |
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+ | 0.0295 | 3.0 | 2631 | 0.0631 | 0.9414 | 0.9456 | 0.9435 | 0.9859 |
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+ | 0.0155 | 4.0 | 3508 | 0.0680 | 0.9395 | 0.9467 | 0.9431 | 0.9860 |
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+ | 0.0097 | 5.0 | 4385 | 0.0694 | 0.9385 | 0.9513 | 0.9449 | 0.9863 |
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+ | 0.0059 | 6.0 | 5262 | 0.0743 | 0.9363 | 0.9471 | 0.9416 | 0.9860 |
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+ | 0.0041 | 7.0 | 6139 | 0.0803 | 0.9371 | 0.9518 | 0.9444 | 0.9862 |
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  ### Framework versions
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