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distilbert-base-uncased-finetuned-ner-lenerBr

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  1. README.md +20 -19
  2. model.safetensors +1 -1
  3. runs/Oct06_23-10-17_2254f489b8be/events.out.tfevents.1728256235.2254f489b8be.35.0 +2 -2
  4. runs/Oct06_23-10-17_2254f489b8be/events.out.tfevents.1728257354.2254f489b8be.35.1 +3 -0
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: 0.7477750426055672
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  - name: Recall
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  type: recall
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- value: 0.8118832236842105
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  - name: F1
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  type: f1
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- value: 0.7785115820601283
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  - name: Accuracy
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  type: accuracy
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- value: 0.9644699967525048
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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 [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the lener_br dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1546
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- - Precision: 0.7478
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- - Recall: 0.8119
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- - F1: 0.7785
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- - Accuracy: 0.9645
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  ## Model description
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@@ -73,21 +73,22 @@ 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: 100
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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 | 490 | 0.2131 | 0.6201 | 0.6604 | 0.6396 | 0.9359 |
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- | 0.264 | 2.0 | 980 | 0.1828 | 0.7004 | 0.7504 | 0.7246 | 0.9508 |
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- | 0.0776 | 3.0 | 1470 | 0.1564 | 0.6582 | 0.8137 | 0.7278 | 0.9537 |
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- | 0.0437 | 4.0 | 1960 | 0.1644 | 0.7485 | 0.7623 | 0.7553 | 0.9573 |
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- | 0.0288 | 5.0 | 2450 | 0.1555 | 0.7620 | 0.7662 | 0.7641 | 0.9614 |
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- | 0.0208 | 6.0 | 2940 | 0.1874 | 0.7530 | 0.7759 | 0.7643 | 0.9550 |
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- | 0.0143 | 7.0 | 3430 | 0.1546 | 0.7478 | 0.8119 | 0.7785 | 0.9645 |
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- | 0.0117 | 8.0 | 3920 | 0.1717 | 0.7014 | 0.7677 | 0.7330 | 0.9592 |
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- | 0.0102 | 9.0 | 4410 | 0.1884 | 0.7734 | 0.7714 | 0.7724 | 0.9613 |
 
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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: 0.7845931433292028
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  - name: Recall
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  type: recall
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+ value: 0.7810444078947368
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  - name: F1
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  type: f1
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+ value: 0.7828147537605605
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9671762427683093
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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 [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the lener_br dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1555
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+ - Precision: 0.7846
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+ - Recall: 0.7810
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+ - F1: 0.7828
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+ - Accuracy: 0.9672
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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 | 490 | 0.1861 | 0.6380 | 0.6661 | 0.6518 | 0.9446 |
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+ | 0.2629 | 2.0 | 980 | 0.1618 | 0.7063 | 0.7303 | 0.7181 | 0.9537 |
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+ | 0.0756 | 3.0 | 1470 | 0.1299 | 0.7299 | 0.8010 | 0.7638 | 0.9645 |
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+ | 0.0443 | 4.0 | 1960 | 0.1422 | 0.7634 | 0.7708 | 0.7671 | 0.9643 |
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+ | 0.0279 | 5.0 | 2450 | 0.1508 | 0.7870 | 0.7679 | 0.7773 | 0.9648 |
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+ | 0.0203 | 6.0 | 2940 | 0.1457 | 0.7693 | 0.7815 | 0.7753 | 0.9681 |
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+ | 0.0143 | 7.0 | 3430 | 0.1508 | 0.7767 | 0.7714 | 0.7740 | 0.9663 |
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+ | 0.0105 | 8.0 | 3920 | 0.1537 | 0.7812 | 0.7669 | 0.7739 | 0.9671 |
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+ | 0.0085 | 9.0 | 4410 | 0.1564 | 0.7809 | 0.7681 | 0.7745 | 0.9669 |
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+ | 0.0064 | 10.0 | 4900 | 0.1555 | 0.7846 | 0.7810 | 0.7828 | 0.9672 |
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  ### Framework versions
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