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Multilingual-Perspectivist-NLU
Multilingual-Perspectivist-NLU/irony_pt_Brazil
irony_pt_Brazil is a text classification model from Multilingual-Perspectivist-NLU. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This model is a fine-tuned version of roberta-base on part of the MultiPICo dataset. It achieves the following results on the evaluation set: - Loss: 0.0033 - Accuracy: 0.6463 - Precision: 0.44 - Recall: 0.5739 - F1:…
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From the Hugging Face model README
This model is a fine-tuned version of roberta-base on part of the MultiPICo dataset. It achieves the following results on the evaluation set:
The model is trained considering the annotation of annotators from Brazil only, on instances in Portuguese (PT and BZ linguistic varieties). The annotations from these annotators are aggregated using majority voting and then used to train the model.
The model has been trained on the annotation from annotators from Brazil from the MultiPICo dataset (instances in Portuguese). The data has been randomly split into a train and a validation set.
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0043 | 1.0 | 71 | 0.0042 | 0.5745 | 0.3448 | 0.4348 | 0.3846 |
| 0.0043 | 2.0 | 142 | 0.0041 | 0.5266 | 0.3562 | 0.6783 | 0.4671 |
| 0.0039 | 3.0 | 213 | 0.0039 | 0.5266 | 0.3562 | 0.6783 | 0.4671 |
| 0.004 | 4.0 | 284 | 0.0038 | 0.6170 | 0.4110 | 0.5826 | 0.4820 |
| 0.0036 | 5.0 | 355 | 0.0035 | 0.6516 | 0.4452 | 0.5652 | 0.4981 |
| 0.0035 | 6.0 | 426 | 0.0036 | 0.4973 | 0.3630 | 0.8522 | 0.5091 |
| 0.0031 | 7.0 | 497 | 0.0033 | 0.5904 | 0.4156 | 0.8348 | 0.5549 |
| 0.0027 | 8.0 | 568 | 0.0033 | 0.6543 | 0.4460 | 0.5391 | 0.4882 |
| 0.0027 | 9.0 | 639 | 0.0031 | 0.6144 | 0.4257 | 0.7478 | 0.5426 |
| 0.0023 | 10.0 | 710 | 0.0029 | 0.6303 | 0.4388 | 0.7478 | 0.5531 |
| 0.0021 | 11.0 | 781 | 0.0031 | 0.6383 | 0.4348 | 0.6087 | 0.5072 |
| 0.0018 | 12.0 | 852 | 0.0033 | 0.6463 | 0.44 | 0.5739 | 0.4981 |