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--- |
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license: mit |
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base_model: neuralmind/bert-base-portuguese-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- f1 |
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- recall |
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model-index: |
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- name: newsdata-bertimbal |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# newsdata-bertimbal |
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This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2924 |
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- Accuracy: 0.9183 |
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- Precision: 0.9118 |
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- F1: 0.9144 |
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- Recall: 0.9183 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.7154 | 0.1024 | 1000 | 0.5830 | 0.856 | 0.8352 | 0.8399 | 0.856 | |
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| 0.5232 | 0.2048 | 2000 | 0.4769 | 0.874 | 0.8647 | 0.8633 | 0.874 | |
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| 0.4342 | 0.3071 | 3000 | 0.3966 | 0.891 | 0.8800 | 0.8826 | 0.891 | |
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| 0.3969 | 0.4095 | 4000 | 0.3509 | 0.9023 | 0.8900 | 0.8949 | 0.9023 | |
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| 0.3719 | 0.5119 | 5000 | 0.3263 | 0.9102 | 0.9055 | 0.9054 | 0.9102 | |
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| 0.3638 | 0.6143 | 6000 | 0.3209 | 0.909 | 0.9017 | 0.9035 | 0.909 | |
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| 0.3217 | 0.7166 | 7000 | 0.3131 | 0.9068 | 0.9025 | 0.9034 | 0.9068 | |
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| 0.3169 | 0.8190 | 8000 | 0.2952 | 0.9167 | 0.9101 | 0.9125 | 0.9167 | |
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| 0.3147 | 0.9214 | 9000 | 0.2924 | 0.9183 | 0.9118 | 0.9144 | 0.9183 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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