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bert-base-spanish-wwm-uncased-finetuned-ner

Token Classification
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  1. README.md +21 -21
  2. model.safetensors +1 -1
  3. runs/Dec05_09-45-28_0ce4b59191ac/events.out.tfevents.1733391950.0ce4b59191ac.9325.3 +2 -2
  4. runs/Dec05_09-45-28_0ce4b59191ac/events.out.tfevents.1733392439.0ce4b59191ac.9325.4 +3 -0
README.md CHANGED
@@ -11,7 +11,7 @@ metrics:
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  - f1
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  - accuracy
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  model-index:
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- - name: bert-biobert
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  results:
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  - task:
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  name: Token Classification
@@ -25,30 +25,30 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9479839607930497
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  - name: Recall
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  type: recall
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- value: 0.9641461342395922
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  - name: F1
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  type: f1
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- value: 0.95599674257954
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  - name: Accuracy
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  type: accuracy
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- value: 0.9768617183218656
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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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  should probably proofread and complete it, then remove this comment. -->
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- # bert-biobert
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  This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1154
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- - Precision: 0.9480
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- - Recall: 0.9641
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- - F1: 0.9560
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- - Accuracy: 0.9769
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  ## Model description
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@@ -79,16 +79,16 @@ 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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- | 0.364 | 1.0 | 612 | 0.1155 | 0.9302 | 0.9525 | 0.9412 | 0.9686 |
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- | 0.117 | 2.0 | 1224 | 0.1034 | 0.9405 | 0.9640 | 0.9521 | 0.9749 |
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- | 0.0837 | 3.0 | 1836 | 0.0980 | 0.9469 | 0.9682 | 0.9574 | 0.9772 |
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- | 0.066 | 4.0 | 2448 | 0.0975 | 0.9474 | 0.9678 | 0.9575 | 0.9775 |
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- | 0.0451 | 5.0 | 3060 | 0.0982 | 0.9498 | 0.9648 | 0.9572 | 0.9770 |
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- | 0.0405 | 6.0 | 3672 | 0.1074 | 0.9469 | 0.9643 | 0.9555 | 0.9761 |
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- | 0.0318 | 7.0 | 4284 | 0.1104 | 0.9478 | 0.9663 | 0.9570 | 0.9771 |
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- | 0.031 | 8.0 | 4896 | 0.1145 | 0.9495 | 0.9654 | 0.9574 | 0.9772 |
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- | 0.0235 | 9.0 | 5508 | 0.1137 | 0.9495 | 0.9639 | 0.9566 | 0.9767 |
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- | 0.0209 | 10.0 | 6120 | 0.1154 | 0.9480 | 0.9641 | 0.9560 | 0.9769 |
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  ### Framework versions
 
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  - f1
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  - accuracy
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  model-index:
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+ - name: bert-base-spanish-wwm-uncased-finetuned-ner
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  results:
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  - task:
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  name: Token Classification
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9483257314495495
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  - name: Recall
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  type: recall
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+ value: 0.9656754460492778
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  - name: F1
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  type: f1
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+ value: 0.9569219543681419
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9766181574620958
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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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  should probably proofread and complete it, then remove this comment. -->
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+ # bert-base-spanish-wwm-uncased-finetuned-ner
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  This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1423
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+ - Precision: 0.9483
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+ - Recall: 0.9657
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+ - F1: 0.9569
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+ - Accuracy: 0.9766
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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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+ | 0.0179 | 1.0 | 612 | 0.1292 | 0.9547 | 0.9629 | 0.9588 | 0.9779 |
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+ | 0.0133 | 2.0 | 1224 | 0.1574 | 0.9463 | 0.9684 | 0.9572 | 0.9766 |
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+ | 0.0146 | 3.0 | 1836 | 0.1179 | 0.9500 | 0.9622 | 0.9561 | 0.9769 |
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+ | 0.0238 | 4.0 | 2448 | 0.1388 | 0.9441 | 0.9677 | 0.9557 | 0.9759 |
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+ | 0.0152 | 5.0 | 3060 | 0.1442 | 0.9430 | 0.9634 | 0.9531 | 0.9754 |
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+ | 0.0155 | 6.0 | 3672 | 0.1401 | 0.9480 | 0.9641 | 0.9560 | 0.9760 |
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+ | 0.0126 | 7.0 | 4284 | 0.1411 | 0.9468 | 0.9676 | 0.9571 | 0.9769 |
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+ | 0.0131 | 8.0 | 4896 | 0.1427 | 0.9484 | 0.9657 | 0.9570 | 0.9767 |
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+ | 0.0117 | 9.0 | 5508 | 0.1391 | 0.9485 | 0.9651 | 0.9567 | 0.9767 |
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+ | 0.0116 | 10.0 | 6120 | 0.1423 | 0.9483 | 0.9657 | 0.9569 | 0.9766 |
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  ### Framework versions
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