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beto-finetuned-ner-cfv

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  1. README.md +26 -31
  2. model.safetensors +1 -1
  3. runs/Jun06_04-06-43_7db57397ed39/events.out.tfevents.1717646804.7db57397ed39.435.0 +3 -0
  4. runs/Jun06_04-07-39_7db57397ed39/events.out.tfevents.1717646862.7db57397ed39.435.1 +3 -0
  5. runs/Jun06_04-07-39_7db57397ed39/events.out.tfevents.1717648853.7db57397ed39.435.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8614471581830633
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  - name: Recall
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  type: recall
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- value: 0.8671875
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  - name: F1
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  type: f1
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- value: 0.8643077980075576
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  - name: Accuracy
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  type: accuracy
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- value: 0.9790072369291234
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1659
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- - Precision: 0.8614
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- - Recall: 0.8672
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- - F1: 0.8643
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- - Accuracy: 0.9790
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  ## Model description
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@@ -67,34 +67,29 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 4e-06
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- - train_batch_size: 8
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- - eval_batch_size: 8
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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: 16
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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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- | 0.0272 | 1.0 | 1041 | 0.1062 | 0.8549 | 0.8637 | 0.8593 | 0.9786 |
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- | 0.0208 | 2.0 | 2082 | 0.1127 | 0.8443 | 0.8596 | 0.8519 | 0.9782 |
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- | 0.0158 | 3.0 | 3123 | 0.1195 | 0.8545 | 0.8598 | 0.8572 | 0.9787 |
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- | 0.0129 | 4.0 | 4164 | 0.1332 | 0.8629 | 0.8589 | 0.8609 | 0.9782 |
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- | 0.0107 | 5.0 | 5205 | 0.1299 | 0.8555 | 0.8635 | 0.8595 | 0.9786 |
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- | 0.0087 | 6.0 | 6246 | 0.1486 | 0.8564 | 0.8564 | 0.8564 | 0.9782 |
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- | 0.0085 | 7.0 | 7287 | 0.1583 | 0.8618 | 0.8596 | 0.8607 | 0.9783 |
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- | 0.0066 | 8.0 | 8328 | 0.1582 | 0.8604 | 0.8580 | 0.8592 | 0.9783 |
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- | 0.0052 | 9.0 | 9369 | 0.1571 | 0.8554 | 0.8566 | 0.8560 | 0.9781 |
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- | 0.0054 | 10.0 | 10410 | 0.1604 | 0.8628 | 0.8640 | 0.8634 | 0.9787 |
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- | 0.004 | 11.0 | 11451 | 0.1584 | 0.8624 | 0.8670 | 0.8647 | 0.9791 |
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- | 0.0036 | 12.0 | 12492 | 0.1633 | 0.8603 | 0.8658 | 0.8630 | 0.9786 |
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- | 0.0036 | 13.0 | 13533 | 0.1620 | 0.8628 | 0.8658 | 0.8643 | 0.9790 |
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- | 0.0032 | 14.0 | 14574 | 0.1645 | 0.8617 | 0.8676 | 0.8647 | 0.9793 |
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- | 0.0028 | 15.0 | 15615 | 0.1645 | 0.8604 | 0.8670 | 0.8637 | 0.9792 |
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- | 0.003 | 16.0 | 16656 | 0.1659 | 0.8614 | 0.8672 | 0.8643 | 0.9790 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.865948670944088
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  - name: Recall
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  type: recall
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+ value: 0.8683363970588235
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  - name: F1
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  type: f1
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+ value: 0.867140890316659
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9792528768210419
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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 [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1667
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+ - Precision: 0.8659
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+ - Recall: 0.8683
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+ - F1: 0.8671
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+ - Accuracy: 0.9793
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 4e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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: 11
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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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+ | 0.0373 | 1.0 | 521 | 0.1002 | 0.8642 | 0.8568 | 0.8605 | 0.9779 |
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+ | 0.0255 | 2.0 | 1042 | 0.1018 | 0.8410 | 0.8555 | 0.8482 | 0.9779 |
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+ | 0.0147 | 3.0 | 1563 | 0.1093 | 0.8654 | 0.8626 | 0.8640 | 0.9789 |
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+ | 0.0107 | 4.0 | 2084 | 0.1277 | 0.8772 | 0.8614 | 0.8692 | 0.9787 |
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+ | 0.0069 | 5.0 | 2605 | 0.1422 | 0.8496 | 0.8529 | 0.8513 | 0.9782 |
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+ | 0.0052 | 6.0 | 3126 | 0.1436 | 0.8511 | 0.8511 | 0.8511 | 0.9775 |
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+ | 0.0039 | 7.0 | 3647 | 0.1515 | 0.8663 | 0.8621 | 0.8642 | 0.9784 |
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+ | 0.0029 | 8.0 | 4168 | 0.1525 | 0.8585 | 0.8617 | 0.8601 | 0.9785 |
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+ | 0.0024 | 9.0 | 4689 | 0.1549 | 0.8635 | 0.8633 | 0.8634 | 0.9784 |
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+ | 0.0021 | 10.0 | 5210 | 0.1643 | 0.8660 | 0.8672 | 0.8666 | 0.9792 |
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+ | 0.0017 | 11.0 | 5731 | 0.1667 | 0.8659 | 0.8683 | 0.8671 | 0.9793 |
 
 
 
 
 
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  ### Framework versions
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