content

This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4768
  • Accuracy: 0.7739
  • F1-score: 0.7823
  • Recall: 0.9002
  • Precision: 0.6917

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: 2.5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy F1-score Recall Precision
0.5016 0.3814 500 0.4686 0.7736 0.7865 0.9022 0.6971
0.4628 0.7628 1000 0.4437 0.7753 0.7769 0.8464 0.7180
0.4139 1.1442 1500 0.4633 0.7773 0.7573 0.7517 0.7630
0.3569 1.5256 2000 0.5019 0.7831 0.7930 0.8991 0.7093
0.357 1.9069 2500 0.4498 0.7839 0.7644 0.7585 0.7704
0.2612 2.2883 3000 0.6906 0.7665 0.7740 0.8650 0.7003
0.2292 2.6697 3500 0.6406 0.7624 0.7711 0.8656 0.6952
0.2345 3.0511 4000 0.8274 0.7687 0.7502 0.7511 0.7492
0.1527 3.4325 4500 0.8778 0.7602 0.7433 0.7511 0.7356
0.1613 3.8139 5000 0.8756 0.7564 0.7220 0.6842 0.7642
0.1188 4.1953 5500 1.2264 0.7567 0.7317 0.7176 0.7463
0.0992 4.5767 6000 1.2104 0.7636 0.7440 0.7430 0.7449
0.0938 4.9580 6500 1.1858 0.7616 0.7461 0.7579 0.7347

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
Downloads last month
19
Safetensors
Model size
334M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for JFrediani/Bertimbau-Large-Offensive

Finetuned
(34)
this model