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: xlm-roberta-base-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: 0.9206349206349206
+    - name: Recall
+      type: recall
+      value: 0.9294391315585423
+    - name: F1
+      type: f1
+      value: 0.925016077170418
+    - name: Accuracy
+      type: accuracy
+      value: 0.9832504071600401
+---
+
+<!-- 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. -->
+
+# xlm-roberta-base-finetuned-lener_br-finetuned-lener-br
+
+This model is a fine-tuned version of [Luciano/xlm-roberta-base-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-base-finetuned-lener_br) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.9206
+- Recall: 0.9294
+- F1: 0.9250
+- Accuracy: 0.9833
+
+## 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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0657        | 1.0   | 1957  | nan             | 0.7780    | 0.8687 | 0.8209 | 0.9718   |
+| 0.0321        | 2.0   | 3914  | nan             | 0.8755    | 0.8708 | 0.8731 | 0.9793   |
+| 0.0274        | 3.0   | 5871  | nan             | 0.8096    | 0.9124 | 0.8579 | 0.9735   |
+| 0.0216        | 4.0   | 7828  | nan             | 0.7913    | 0.8842 | 0.8352 | 0.9718   |
+| 0.0175        | 5.0   | 9785  | nan             | 0.7735    | 0.9248 | 0.8424 | 0.9721   |
+| 0.0117        | 6.0   | 11742 | nan             | 0.9206    | 0.9294 | 0.9250 | 0.9833   |
+| 0.0121        | 7.0   | 13699 | nan             | 0.8988    | 0.9318 | 0.9150 | 0.9819   |
+| 0.0086        | 8.0   | 15656 | nan             | 0.8922    | 0.9175 | 0.9047 | 0.9801   |
+| 0.007         | 9.0   | 17613 | nan             | 0.8482    | 0.8997 | 0.8732 | 0.9769   |
+| 0.0051        | 10.0  | 19570 | nan             | 0.8730    | 0.9274 | 0.8994 | 0.9798   |
+| 0.0045        | 11.0  | 21527 | nan             | 0.9172    | 0.9051 | 0.9111 | 0.9819   |
+| 0.0014        | 12.0  | 23484 | nan             | 0.9138    | 0.9155 | 0.9147 | 0.9823   |
+| 0.0029        | 13.0  | 25441 | nan             | 0.9099    | 0.9287 | 0.9192 | 0.9834   |
+| 0.0035        | 14.0  | 27398 | nan             | 0.9019    | 0.9294 | 0.9155 | 0.9831   |
+| 0.0005        | 15.0  | 29355 | nan             | 0.8886    | 0.9343 | 0.9109 | 0.9825   |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1