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bert-finetuned-ner-1

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 ---
-library_name: transformers
-tags: []
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-# Model Card for Model ID
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 ---
+base_model: google-bert/bert-base-cased
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-finetuned-ner-1
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2002
+      type: conll2002
+      config: es
+      split: validation
+      args: es
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.805356
+    - name: Recall
+      type: recall
+      value: 0.822381
+    - name: F1
+      type: f1
+      value: 0.813779
+    - name: Accuracy
+      type: accuracy
+      value: 0.969573
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# xml-roberta-large-finetuned-ner
+Este es modelo resultado de un finetuning de
+[google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) sobre el conll2002 dataset.
+Los siguientes son los resultados sobre el conjunto de evaluación:
+- Training Loss: 0.000900
+- Validation Loss: 0.306902
+- Precision: 0.805356
+- Recall: 0.822381
+- F1: 0.813779
+- Accuracy: 0.969573
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- num_epochs: 8
+- weight_decay: 0.001
+### Training results
+| Epoch | Training Loss | Validation Loss | Precision | Recall  | F1      | Accuracy |
+|:-----:|:-------------:|:---------------:|:---------:|:-------:|:-------:|:--------:|
+| 1.0   | 0.0045        | 0.263534        | 0.787187  | 0.815947| 0.801309| 0.968117 |
+| 2.0   | 0.0054        | 0.261010        | 0.776933  | 0.798713| 0.787673| 0.966914 |
+| 3.0   | 0.0031        | 0.288264        | 0.787994  | 0.811351| 0.799502| 0.967351 |
+| 4.0   | 0.0030        | 0.261651        | 0.799186  | 0.812040| 0.805562| 0.969476 |
+| 5.0   | 0.0023        | 0.281675        | 0.792880  | 0.813649| 0.803130| 0.968544 |
+| 6.0   | 0.0014        | 0.285965        | 0.790842  | 0.817555| 0.803977| 0.969311 |
+| 7.0   | 0.0009        | 0.320790        | 0.795583  | 0.811121| 0.803277| 0.968049 |
+| 8.0   | 0.0009        | 0.306902        | 0.805356  | 0.822381| 0.813779| 0.969573 |