| --- |
| license: mit |
| base_model: neuralmind/bert-base-portuguese-cased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - __main__ |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: absa_model_v1 |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: __main__ |
| type: __main__ |
| config: local |
| split: test |
| args: local |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.4978690430065866 |
| - name: Recall |
| type: recall |
| value: 0.5325321176958143 |
| - name: F1 |
| type: f1 |
| value: 0.514617541049259 |
| - name: Accuracy |
| type: accuracy |
| value: 0.7477374784110535 |
| --- |
| |
| <!-- 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. --> |
|
|
| # absa_model_v1 |
|
|
| This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the __main__ dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7541 |
| - Precision: 0.4979 |
| - Recall: 0.5325 |
| - F1: 0.5146 |
| - Accuracy: 0.7477 |
|
|
| ## 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: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.7317 | 1.0 | 5905 | 0.7541 | 0.4979 | 0.5325 | 0.5146 | 0.7477 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.36.0 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.4 |
| - Tokenizers 0.15.0 |
|
|
