End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: neuralmind/bert-base-portuguese-cased
+tags:
+- generated_from_trainer
+model-index:
+- name: bbau-semeval25_fold4
+  results: []
+---
+
+<!-- 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. -->
+
+# bbau-semeval25_fold4
+
+This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4458
+- Precision Samples: 1.0
+- Recall Samples: 0.0
+- F1 Samples: 0.0
+- Precision Macro: 1.0
+- Recall Macro: 0.4091
+- F1 Macro: 0.4091
+- Precision Micro: 1.0
+- Recall Micro: 0.0
+- F1 Micro: 0.0
+- Precision Weighted: 1.0
+- Recall Weighted: 0.0
+- F1 Weighted: 0.0
+
+## 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: Use OptimizerNames.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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision Samples | Recall Samples | F1 Samples | Precision Macro | Recall Macro | F1 Macro | Precision Micro | Recall Micro | F1 Micro | Precision Weighted | Recall Weighted | F1 Weighted |
+|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
+| No log        | 1.0   | 5    | 0.6306          | 0.0699            | 0.2911         | 0.1095     | 0.5166          | 0.6006       | 0.2907   | 0.0704          | 0.3585       | 0.1176   | 0.4119             | 0.3585          | 0.1190      |
+| 0.6408        | 2.0   | 10   | 0.5798          | 0.0481            | 0.1398         | 0.0666     | 0.7414          | 0.4794       | 0.3825   | 0.0505          | 0.1321       | 0.0731   | 0.5897             | 0.1321          | 0.0513      |
+| 0.6408        | 3.0   | 15   | 0.5419          | 0.05              | 0.0175         | 0.0243     | 0.8951          | 0.4394       | 0.3810   | 0.0326          | 0.0283       | 0.0303   | 0.8880             | 0.0283          | 0.0023      |
+| 0.5512        | 4.0   | 20   | 0.5123          | 0.0625            | 0.0092         | 0.0143     | 0.9705          | 0.4242       | 0.4106   | 0.0392          | 0.0189       | 0.0255   | 0.9727             | 0.0189          | 0.0018      |
+| 0.5512        | 5.0   | 25   | 0.4900          | 0.975             | 0.005          | 0.0083     | 0.9924          | 0.4167       | 0.4167   | 0.5             | 0.0094       | 0.0185   | 0.9906             | 0.0094          | 0.0094      |
+| 0.5002        | 6.0   | 30   | 0.4730          | 1.0               | 0.0            | 0.0        | 1.0             | 0.4091       | 0.4091   | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
+| 0.5002        | 7.0   | 35   | 0.4612          | 1.0               | 0.0            | 0.0        | 1.0             | 0.4091       | 0.4091   | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
+| 0.4705        | 8.0   | 40   | 0.4529          | 1.0               | 0.0            | 0.0        | 1.0             | 0.4091       | 0.4091   | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
+| 0.4705        | 9.0   | 45   | 0.4477          | 1.0               | 0.0            | 0.0        | 1.0             | 0.4091       | 0.4091   | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
+| 0.4562        | 10.0  | 50   | 0.4458          | 1.0               | 0.0            | 0.0        | 1.0             | 0.4091       | 0.4091   | 1.0             | 0.0          | 0.0      | 1.0                | 0.0             | 0.0         |
+
+
+### Framework versions
+
+- Transformers 4.46.0
+- Pytorch 2.3.1
+- Datasets 2.21.0
+- Tokenizers 0.20.1

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