Create README.md

README.md

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+---
+language:
+- pt
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-multilingual-cased-finetuned-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: 0.9122490993309316
+    - name: Recall
+      type: recall
+      value: 0.9162574308606876
+    - name: F1
+      type: f1
+      value: 0.9142488716956804
+    - name: Accuracy
+      type: accuracy
+      value: 0.982592974434832
+---
+
+<!-- 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. -->
+
+# bert-base-multilingual-cased-finetuned-lener_br-finetuned-lener-br
+
+This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan  (To update)
+- Precision: 0.9122  (To update)
+- Recall: 0.9163  (To update)
+- F1: 0.9142  (To update)
+- Accuracy: 0.9826  (To update)
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters (To update) 
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results (To update)
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.068         | 1.0   | 3914  | nan             | 0.6196    | 0.8604 | 0.7204 | 0.9568   |
+| 0.0767        | 2.0   | 7828  | nan             | 0.8270    | 0.8710 | 0.8484 | 0.9693   |
+| 0.0257        | 3.0   | 11742 | nan             | 0.7243    | 0.9005 | 0.8029 | 0.9639   |
+| 0.0193        | 4.0   | 15656 | nan             | 0.9010    | 0.8984 | 0.8997 | 0.9821   |
+| 0.0156        | 5.0   | 19570 | nan             | 0.7150    | 0.9121 | 0.8016 | 0.9641   |
+| 0.0165        | 6.0   | 23484 | nan             | 0.7640    | 0.8796 | 0.8177 | 0.9691   |
+| 0.0225        | 7.0   | 27398 | nan             | 0.8851    | 0.9098 | 0.8973 | 0.9803   |
+| 0.016         | 8.0   | 31312 | nan             | 0.9081    | 0.9015 | 0.9048 | 0.9792   |
+| 0.0078        | 9.0   | 35226 | nan             | 0.8941    | 0.8863 | 0.8902 | 0.9788   |
+| 0.0061        | 10.0  | 39140 | nan             | 0.9026    | 0.9002 | 0.9014 | 0.9804   |
+| 0.0057        | 11.0  | 43054 | nan             | 0.8793    | 0.9018 | 0.8904 | 0.9769   |
+| 0.0044        | 12.0  | 46968 | nan             | 0.8790    | 0.9033 | 0.8910 | 0.9785   |
+| 0.0043        | 13.0  | 50882 | nan             | 0.9122    | 0.9163 | 0.9142 | 0.9826   |
+| 0.0003        | 14.0  | 54796 | nan             | 0.9032    | 0.9070 | 0.9051 | 0.9807   |
+| 0.0025        | 15.0  | 58710 | nan             | 0.8903    | 0.9085 | 0.8993 | 0.9798   |
+
+
+### Framework versions (To update)
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1