Training complete - BERTimbau-base-LeNER

README.md

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+---
+license: mit
+base_model: neuralmind/bert-base-portuguese-cased
+tags:
+- generated_from_trainer
+datasets:
+- lener_br
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: BERTimbau-base_LeNER-Br
+  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: 0.8317805383022774
+    - name: Recall
+      type: recall
+      value: 0.8839383938393839
+    - name: F1
+      type: f1
+      value: 0.8570666666666666
+    - name: Accuracy
+      type: accuracy
+      value: 0.9754369390647142
+---
+
+<!-- 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. -->
+
+# BERTimbau-base_LeNER-Br
+
+This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the lener_br dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Precision: 0.8318
+- Recall: 0.8839
+- F1: 0.8571
+- Accuracy: 0.9754
+
+## 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: 8
+- eval_batch_size: 8
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2037        | 1.0   | 979  | nan             | 0.7910    | 0.8762 | 0.8314 | 0.9721   |
+| 0.0308        | 2.0   | 1958 | nan             | 0.7747    | 0.8663 | 0.8180 | 0.9698   |
+| 0.02          | 3.0   | 2937 | nan             | 0.8316    | 0.8911 | 0.8603 | 0.9801   |
+| 0.0133        | 4.0   | 3916 | nan             | 0.8038    | 0.8812 | 0.8407 | 0.9728   |
+| 0.0111        | 5.0   | 4895 | nan             | 0.8253    | 0.8707 | 0.8474 | 0.9753   |
+| 0.0078        | 6.0   | 5874 | nan             | 0.8235    | 0.8779 | 0.8498 | 0.9711   |
+| 0.0057        | 7.0   | 6853 | nan             | 0.8174    | 0.8768 | 0.8461 | 0.9760   |
+| 0.0032        | 8.0   | 7832 | nan             | 0.8113    | 0.8845 | 0.8463 | 0.9769   |
+| 0.0027        | 9.0   | 8811 | nan             | 0.8344    | 0.8867 | 0.8597 | 0.9767   |
+| 0.0023        | 10.0  | 9790 | nan             | 0.8318    | 0.8839 | 0.8571 | 0.9754   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1