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-large-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: 0.8545767716535433
+    - name: Recall
+      type: recall
+      value: 0.8976479710519514
+    - name: F1
+      type: f1
+      value: 0.8755830076893987
+    - name: Accuracy
+      type: accuracy
+      value: 0.979126510974644
+---
+
+<!-- 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-large-finetuned-lener-br
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.8546
+- Recall: 0.8976
+- F1: 0.8756
+- Accuracy: 0.9791
+
+## 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: 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
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0836        | 1.0   | 3914  | nan             | 0.5735    | 0.8348 | 0.6799 | 0.9526   |
+| 0.0664        | 2.0   | 7828  | nan             | 0.8153    | 0.8315 | 0.8233 | 0.9658   |
+| 0.0505        | 3.0   | 11742 | nan             | 0.6885    | 0.9147 | 0.7857 | 0.9644   |
+| 0.1165        | 4.0   | 15656 | nan             | 0.7572    | 0.8067 | 0.7811 | 0.9641   |
+| 0.0206        | 5.0   | 19570 | nan             | 0.8678    | 0.8770 | 0.8723 | 0.9774   |
+| 0.02          | 6.0   | 23484 | nan             | 0.7285    | 0.8907 | 0.8015 | 0.9669   |
+| 0.0248        | 7.0   | 27398 | nan             | 0.8717    | 0.9095 | 0.8902 | 0.9793   |
+| 0.0223        | 8.0   | 31312 | nan             | 0.8407    | 0.8801 | 0.8600 | 0.9766   |
+| 0.0084        | 9.0   | 35226 | nan             | 0.8354    | 0.8684 | 0.8516 | 0.9705   |
+| 0.0067        | 10.0  | 39140 | nan             | 0.8312    | 0.9062 | 0.8671 | 0.9753   |
+| 0.006         | 11.0  | 43054 | nan             | 0.8866    | 0.8953 | 0.8909 | 0.9784   |
+| 0.0058        | 12.0  | 46968 | nan             | 0.8961    | 0.8987 | 0.8974 | 0.9807   |
+| 0.0062        | 13.0  | 50882 | nan             | 0.8360    | 0.8785 | 0.8567 | 0.9783   |
+| 0.0053        | 14.0  | 54796 | nan             | 0.8327    | 0.8749 | 0.8533 | 0.9782   |
+| 0.003         | 15.0  | 58710 | nan             | 0.8546    | 0.8976 | 0.8756 | 0.9791   |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1