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--- |
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language: |
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- pt |
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license: apache-2.0 |
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tags: |
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- toxicity |
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- portuguese |
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- hate speech |
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- offensive language |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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base_model: neuralmind/bert-large-portuguese-cased |
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model-index: |
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- name: dougtrajano/toxicity-target-type-identification |
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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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# dougtrajano/toxicity-target-type-identification |
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This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the OLID-BR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4281 |
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- Accuracy: 0.8002 |
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- F1: 0.7986 |
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- Precision: 0.7990 |
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- Recall: 0.8002 |
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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: 3.952388499692274e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 1993 |
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- optimizer: Adam with betas=(0.9944095815441554,0.8750000522553327) and epsilon=1.8526084265228802e-07 |
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- lr_scheduler_type: linear |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 355 | 0.7145 | 0.6903 | 0.7052 | 0.7528 | 0.6903 | |
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| 0.8011 | 2.0 | 710 | 0.9930 | 0.7928 | 0.7840 | 0.7835 | 0.7928 | |
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| 0.529 | 3.0 | 1065 | 1.4281 | 0.8002 | 0.7986 | 0.7990 | 0.8002 | |
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| 0.529 | 4.0 | 1420 | 1.6783 | 0.7727 | 0.7753 | 0.7788 | 0.7727 | |
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| 0.2706 | 5.0 | 1775 | 2.3904 | 0.7727 | 0.7683 | 0.7660 | 0.7727 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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