Downloads · 30 days
3
10% of all-time downloads
azmn-bertimbau-posicao
belisards/azmn-bertimbau-posicao
azmn-bertimbau-posicao is a text classification model from belisards. 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.
should probably proofread and complete it, then remove this comment. --
All-time downloads
30
Public
Parameters
109M
491 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors436 MB · 88%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Dec 11, 2024
- Updated
- Dec 11, 2024
- SHA
- b1a72d58
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
neuralmind/bert-base-portuguese-cased
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0505
- Accuracy: 0.7211
- F1: 0.6737
- Recall: 0.7341
- Precision: 0.6706
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 5151
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 120
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.0685 | 1.0 | 18 | 0.0667 | 0.3571 | 0.3563 | 0.4877 | 0.4864 |
| 0.0642 | 2.0 | 36 | 0.0655 | 0.5268 | 0.5020 | 0.5461 | 0.5354 |
| 0.0629 | 3.0 | 54 | 0.0641 | 0.6607 | 0.6052 | 0.6253 | 0.6036 |
| 0.0614 | 4.0 | 72 | 0.0618 | 0.6964 | 0.6569 | 0.6942 | 0.6551 |
| 0.0583 | 5.0 | 90 | 0.0584 | 0.7054 | 0.6773 | 0.7339 | 0.6816 |
| 0.0549 | 6.0 | 108 | 0.0548 | 0.7321 | 0.6930 | 0.7295 | 0.6862 |
| 0.048 | 7.0 | 126 | 0.0553 | 0.7768 | 0.7124 | 0.7148 | 0.7102 |
| 0.0391 | 8.0 | 144 | 0.0521 | 0.7768 | 0.7460 | 0.7933 | 0.7360 |
| 0.032 | 9.0 | 162 | 0.0523 | 0.7679 | 0.7208 | 0.7424 | 0.7103 |
| 0.0222 | 10.0 | 180 | 0.0585 | 0.7946 | 0.7354 | 0.7381 | 0.7329 |
| 0.0181 | 11.0 | 198 | 0.0809 | 0.8036 | 0.7083 | 0.6880 | 0.7561 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Dec 11, 2024
- Updated
- Dec 11, 2024