File size: 1,960 Bytes
a87c8fd 4959559 a87c8fd 6a695a1 a87c8fd 6a695a1 a87c8fd 6a695a1 a87c8fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hate_BERTimbau_v1
results: []
---
<!-- 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. -->
# hate_BERTimbau_v1
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the Fortuna et al (2019) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0655
- Precision: 0.7690
- Recall: 0.7690
- F1: 0.7690
- Accuracy: 0.7690
## 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: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5264 | 1.0 | 284 | 0.5050 | 0.7619 | 0.7619 | 0.7619 | 0.7619 |
| 0.4133 | 2.0 | 568 | 0.4516 | 0.7937 | 0.7937 | 0.7937 | 0.7937 |
| 0.289 | 3.0 | 852 | 0.6485 | 0.7531 | 0.7531 | 0.7531 | 0.7531 |
| 0.1804 | 4.0 | 1136 | 0.7828 | 0.7813 | 0.7813 | 0.7813 | 0.7813 |
| 0.1147 | 5.0 | 1420 | 1.0655 | 0.7690 | 0.7690 | 0.7690 | 0.7690 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|