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NERDE-base
Gpaiva/NERDE-base
NERDE-base is a token classification model from Gpaiva. Use it when you need labels on individual words, such as names. It is set up for transformers.
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Model card
From the Hugging Face model README
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NERDE-base
This model is a fine-tuned version of pierreguillou/bert-base-cased-pt-lenerbr on the nerde dataset. It achieves the following results on the evaluation set:
- Loss: 0.1246
- Precision: 0.9119
- Recall: 0.9153
- F1: 0.9136
- Accuracy: 0.9842
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 |
|---|---|---|---|---|---|---|---|
| 0.2466 | 1.0 | 541 | 0.1003 | 0.8515 | 0.8822 | 0.8666 | 0.9782 |
| 0.0608 | 2.0 | 1082 | 0.0855 | 0.8990 | 0.9083 | 0.9036 | 0.9837 |
| 0.0411 | 3.0 | 1623 | 0.1006 | 0.9078 | 0.9103 | 0.9090 | 0.9837 |
| 0.0266 | 4.0 | 2164 | 0.1052 | 0.9023 | 0.9163 | 0.9092 | 0.9828 |
| 0.0191 | 5.0 | 2705 | 0.1060 | 0.9112 | 0.9183 | 0.9147 | 0.9847 |
| 0.0153 | 6.0 | 3246 | 0.1152 | 0.9052 | 0.9098 | 0.9075 | 0.9831 |
| 0.0124 | 7.0 | 3787 | 0.1209 | 0.9029 | 0.9185 | 0.9107 | 0.9835 |
| 0.0083 | 8.0 | 4328 | 0.1176 | 0.9072 | 0.9163 | 0.9117 | 0.9844 |
| 0.0077 | 9.0 | 4869 | 0.1240 | 0.9080 | 0.9201 | 0.9140 | 0.9844 |
| 0.0051 | 10.0 | 5410 | 0.1246 | 0.9119 | 0.9153 | 0.9136 | 0.9842 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Created
- Jul 24, 2022
- Updated
- Sep 3, 2023