Instructions to use Dulfary/roberta-large-bne-capitel-ner_spanish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dulfary/roberta-large-bne-capitel-ner_spanish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Dulfary/roberta-large-bne-capitel-ner_spanish")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Dulfary/roberta-large-bne-capitel-ner_spanish") model = AutoModelForTokenClassification.from_pretrained("Dulfary/roberta-large-bne-capitel-ner_spanish", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-large-bne-capitel-ner_spanish
This model is a fine-tuned version of PlanTL-GOB-ES/roberta-large-bne-capitel-ner on the MEDDOCAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.1915
- Precision: 0.8269
- Recall: 0.6719
- F1: 0.7414
- Accuracy: 0.9561
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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