End of training

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-multilingual-uncased
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-base-multilingual-uncased-finetuned-ner-lenerBR
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lener_br
+      type: lener_br
+      config: lener_br
+      split: validation
+      args: lener_br
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8678256070640177
+    - name: Recall
+      type: recall
+      value: 0.8758006126427179
+    - name: F1
+      type: f1
+      value: 0.8717948717948718
+    - name: Accuracy
+      type: accuracy
+      value: 0.9706569722150091
+---
+
+<!-- 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-uncased-finetuned-ner-lenerBR
+
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1568
+- Precision: 0.8678
+- Recall: 0.8758
+- F1: 0.8718
+- Accuracy: 0.9707
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 245  | 0.1819          | 0.7691    | 0.8118 | 0.7899 | 0.9585   |
+| No log        | 2.0   | 490  | 0.1487          | 0.7383    | 0.8098 | 0.7724 | 0.9586   |
+| 0.1325        | 3.0   | 735  | 0.1532          | 0.8662    | 0.8777 | 0.8719 | 0.9683   |
+| 0.1325        | 4.0   | 980  | 0.1470          | 0.8770    | 0.8800 | 0.8785 | 0.9698   |
+| 0.0233        | 5.0   | 1225 | 0.1155          | 0.8493    | 0.8839 | 0.8663 | 0.9750   |
+| 0.0233        | 6.0   | 1470 | 0.1727          | 0.8874    | 0.8822 | 0.8848 | 0.9701   |
+| 0.0126        | 7.0   | 1715 | 0.1698          | 0.8890    | 0.8853 | 0.8871 | 0.9710   |
+| 0.0126        | 8.0   | 1960 | 0.1687          | 0.8651    | 0.8783 | 0.8716 | 0.9702   |
+| 0.0076        | 9.0   | 2205 | 0.1593          | 0.8077    | 0.8797 | 0.8422 | 0.9668   |
+| 0.0076        | 10.0  | 2450 | 0.1568          | 0.8678    | 0.8758 | 0.8718 | 0.9707   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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