update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bertimabau-base-lener-br-finetuned-lener-br
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lener_br
+      type: lener_br
+      config: lener_br
+      split: train
+      args: lener_br
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8679441782961883
+    - name: Recall
+      type: recall
+      value: 0.8961290322580645
+    - name: F1
+      type: f1
+      value: 0.8818114485239656
+    - name: Accuracy
+      type: accuracy
+      value: 0.9760769195605468
+---
+
+<!-- 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. -->
+
+# bertimabau-base-lener-br-finetuned-lener-br
+
+This model is a fine-tuned version of [Luciano/bert-base-portuguese-cased-finetuned-lener-br](https://huggingface.co/Luciano/bert-base-portuguese-cased-finetuned-lener-br) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.8679
+- Recall: 0.8961
+- F1: 0.8818
+- Accuracy: 0.9761
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0706        | 1.0   | 1957  | nan             | 0.8291    | 0.8460 | 0.8375 | 0.9660   |
+| 0.037         | 2.0   | 3914  | nan             | 0.8403    | 0.8849 | 0.8621 | 0.9659   |
+| 0.0278        | 3.0   | 5871  | nan             | 0.8470    | 0.9118 | 0.8782 | 0.9736   |
+| 0.0218        | 4.0   | 7828  | nan             | 0.8429    | 0.8789 | 0.8605 | 0.9706   |
+| 0.0146        | 5.0   | 9785  | nan             | 0.8216    | 0.9034 | 0.8606 | 0.9725   |
+| 0.0145        | 6.0   | 11742 | nan             | 0.8552    | 0.8940 | 0.8741 | 0.9701   |
+| 0.0098        | 7.0   | 13699 | nan             | 0.8697    | 0.9    | 0.8846 | 0.9752   |
+| 0.0074        | 8.0   | 15656 | nan             | 0.8310    | 0.8862 | 0.8577 | 0.9655   |
+| 0.0053        | 9.0   | 17613 | nan             | 0.8767    | 0.8852 | 0.8809 | 0.9738   |
+| 0.0035        | 10.0  | 19570 | nan             | 0.8328    | 0.8796 | 0.8556 | 0.9714   |
+| 0.0029        | 11.0  | 21527 | nan             | 0.8679    | 0.8974 | 0.8824 | 0.9746   |
+| 0.0014        | 12.0  | 23484 | nan             | 0.8566    | 0.8813 | 0.8688 | 0.9735   |
+| 0.0021        | 13.0  | 25441 | nan             | 0.8842    | 0.8880 | 0.8861 | 0.9754   |
+| 0.0031        | 14.0  | 27398 | nan             | 0.8677    | 0.8987 | 0.8829 | 0.9762   |
+| 0.0008        | 15.0  | 29355 | nan             | 0.8679    | 0.8961 | 0.8818 | 0.9761   |
+
+
+### Framework versions
+
+- Transformers 4.21.2
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1