Instructions to use ciriatico/dodfminer_lite-classification_bertimbau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ciriatico/dodfminer_lite-classification_bertimbau with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ciriatico/dodfminer_lite-classification_bertimbau")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ciriatico/dodfminer_lite-classification_bertimbau") model = AutoModelForSequenceClassification.from_pretrained("ciriatico/dodfminer_lite-classification_bertimbau") - Notebooks
- Google Colab
- Kaggle
bert_portuguese_classification
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: 0.0242
- F1: 0.9967
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: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- 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 | F1 | Validation Loss |
|---|---|---|---|---|
| 0.0749 | 1.0 | 952 | 0.9746 | 0.0969 |
| 0.0011 | 2.0 | 1904 | 0.9949 | 0.0317 |
| 0.0005 | 3.0 | 2856 | 0.9975 | 0.0200 |
| 0.0003 | 4.0 | 3808 | 0.0199 | 0.9967 |
| 0.0001 | 5.0 | 4760 | 0.0242 | 0.9967 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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Model tree for ciriatico/dodfminer_lite-classification_bertimbau
Base model
neuralmind/bert-base-portuguese-cased