Instructions to use JuanC513/beto-ner-prostata-bs8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuanC513/beto-ner-prostata-bs8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="JuanC513/beto-ner-prostata-bs8")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("JuanC513/beto-ner-prostata-bs8") model = AutoModelForTokenClassification.from_pretrained("JuanC513/beto-ner-prostata-bs8") - Notebooks
- Google Colab
- Kaggle
beto-ner-prostata-bs8
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0720
- F1: 0.9631
- Precision: 0.9598
- Recall: 0.9666
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 OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|---|---|---|---|---|---|---|
| No log | 1.0 | 195 | 0.1610 | 0.8899 | 0.8725 | 0.9081 |
| No log | 2.0 | 390 | 0.0786 | 0.9440 | 0.9502 | 0.9379 |
| 0.4032 | 3.0 | 585 | 0.0464 | 0.9642 | 0.9620 | 0.9663 |
| 0.4032 | 4.0 | 780 | 0.0493 | 0.9683 | 0.9683 | 0.9683 |
| 0.4032 | 5.0 | 975 | 0.0474 | 0.9784 | 0.9762 | 0.9806 |
| 0.0525 | 6.0 | 1170 | 0.0422 | 0.9786 | 0.9805 | 0.9767 |
| 0.0525 | 7.0 | 1365 | 0.0457 | 0.9789 | 0.9818 | 0.9761 |
| 0.0264 | 8.0 | 1560 | 0.0492 | 0.9812 | 0.9825 | 0.9799 |
| 0.0264 | 9.0 | 1755 | 0.0449 | 0.9796 | 0.9812 | 0.9780 |
| 0.0264 | 10.0 | 1950 | 0.0448 | 0.9815 | 0.9831 | 0.9799 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for JuanC513/beto-ner-prostata-bs8
Base model
dccuchile/bert-base-spanish-wwm-cased