Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use leofn3/modelo_racismo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leofn3/modelo_racismo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leofn3/modelo_racismo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leofn3/modelo_racismo") model = AutoModelForSequenceClassification.from_pretrained("leofn3/modelo_racismo") - Notebooks
- Google Colab
- Kaggle
modelo_racismo
This model is a fine-tuned version of PORTULAN/albertina-ptbr on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0036
- Accuracy: 0.9989
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 468 | 0.2304 | 0.9583 |
| 0.7037 | 2.0 | 936 | 0.0847 | 0.9840 |
| 0.256 | 3.0 | 1404 | 0.0075 | 0.9979 |
| 0.0759 | 4.0 | 1872 | 0.0036 | 0.9989 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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