metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- told-br
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: bert-base-multilingual-cased-finetuned-hate-speech-ptbr
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: told-br
type: told-br
config: binary
split: validation
args: binary
metrics:
- name: Precision
type: precision
value: 0.702020202020202
- name: Recall
type: recall
value: 0.7654185022026432
- name: Accuracy
type: accuracy
value: 0.758095238095238
- name: F1
type: f1
value: 0.7590123199739615
bert-base-multilingual-cased-finetuned-hate-speech-ptbr
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on the told-br dataset. It achieves the following results on the evaluation set:
- Loss: 0.6224
- Precision: 0.7020
- Recall: 0.7654
- Accuracy: 0.7581
- F1: 0.7590
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|---|---|---|---|---|---|---|---|
| 0.5127 | 1.0 | 1050 | 0.4978 | 0.6500 | 0.8756 | 0.7424 | 0.7418 |
| 0.4415 | 2.0 | 2100 | 0.5206 | 0.7143 | 0.7104 | 0.7519 | 0.7518 |
| 0.3623 | 3.0 | 3150 | 0.6204 | 0.6747 | 0.8293 | 0.7533 | 0.7542 |
| 0.283 | 4.0 | 4200 | 0.6224 | 0.7020 | 0.7654 | 0.7581 | 0.7590 |
| 0.2196 | 5.0 | 5250 | 0.7572 | 0.6954 | 0.7742 | 0.7557 | 0.7568 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3