Instructions to use Ben0ni/xlmroberta-ner-prostata-bs8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ben0ni/xlmroberta-ner-prostata-bs8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ben0ni/xlmroberta-ner-prostata-bs8")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Ben0ni/xlmroberta-ner-prostata-bs8") model = AutoModelForTokenClassification.from_pretrained("Ben0ni/xlmroberta-ner-prostata-bs8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xlmroberta-ner-prostata-bs8
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0636
- Precision: 0.9673
- Recall: 0.9726
- F1: 0.9699
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.8122 | 1.0 | 195 | 0.3194 | 0.7609 | 0.7909 | 0.7756 |
| 0.2060 | 2.0 | 390 | 0.1058 | 0.9227 | 0.9430 | 0.9328 |
| 0.1159 | 3.0 | 585 | 0.0602 | 0.9539 | 0.9638 | 0.9588 |
| 0.0948 | 4.0 | 780 | 0.0555 | 0.9585 | 0.9715 | 0.9650 |
| 0.0614 | 5.0 | 975 | 0.0390 | 0.9780 | 0.9786 | 0.9783 |
| 0.0641 | 6.0 | 1170 | 0.0347 | 0.9792 | 0.9761 | 0.9776 |
| 0.0423 | 7.0 | 1365 | 0.0362 | 0.9819 | 0.9806 | 0.9812 |
| 0.0303 | 8.0 | 1560 | 0.0349 | 0.9831 | 0.9819 | 0.9825 |
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 Ben0ni/xlmroberta-ner-prostata-bs8
Base model
FacebookAI/xlm-roberta-base