End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-base-multilingual-cased
+tags:
+- generated_from_trainer
+datasets:
+- told-br
+metrics:
+- precision
+- recall
+- accuracy
+- f1
+model-index:
+- name: bert-base-multilingual-cased-finetuned-hate-speech-ptbr
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: told-br
+      type: told-br
+      config: binary
+      split: validation
+      args: binary
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.702020202020202
+    - name: Recall
+      type: recall
+      value: 0.7654185022026432
+    - name: Accuracy
+      type: accuracy
+      value: 0.758095238095238
+    - name: F1
+      type: f1
+      value: 0.7590123199739615
+---
+
+<!-- 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-hate-speech-ptbr
+
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the told-br dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6224
+- Precision: 0.7020
+- Recall: 0.7654
+- Accuracy: 0.7581
+- F1: 0.7590
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
+| 0.5127        | 1.0   | 1050 | 0.4978          | 0.6500    | 0.8756 | 0.7424   | 0.7418 |
+| 0.4415        | 2.0   | 2100 | 0.5206          | 0.7143    | 0.7104 | 0.7519   | 0.7518 |
+| 0.3623        | 3.0   | 3150 | 0.6204          | 0.6747    | 0.8293 | 0.7533   | 0.7542 |
+| 0.283         | 4.0   | 4200 | 0.6224          | 0.7020    | 0.7654 | 0.7581   | 0.7590 |
+| 0.2196        | 5.0   | 5250 | 0.7572          | 0.6954    | 0.7742 | 0.7557   | 0.7568 |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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