# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner")Quick Links
nees-bert-base-portuguese-cased-finetuned-ner
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0008
- Precision: 0.6804
- Recall: 0.9301
- F1: 0.7859
- Accuracy: 0.9997
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0009 | 1.0 | 3534 | 0.0008 | 0.6804 | 0.9301 | 0.7859 | 0.9997 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner
Base model
neuralmind/bert-base-portuguese-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="yuridrcosta/nees-bert-base-portuguese-cased-finetuned-ner")