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---
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library_name: transformers
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license: apache-2.0
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base_model: BSC-LT/roberta-base-bne-capitel-ner
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tags:
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- generated_from_trainer
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datasets:
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- conll2002
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: bert-finetuned-ner
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: conll2002
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type: conll2002
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config: es
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split: validation
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args: es
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metrics:
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- name: Precision
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type: precision
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value: 0.8599099099099099
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- name: Recall
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type: recall
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value: 0.8772977941176471
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- name: F1
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type: f1
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value: 0.8685168334849864
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- name: Accuracy
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type: accuracy
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value: 0.978701639744725
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bert-finetuned-ner
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This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0950
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- Precision: 0.8599
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- Recall: 0.8773
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- F1: 0.8685
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- Accuracy: 0.9787
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.1045 | 1.0 | 521 | 0.0932 | 0.8593 | 0.8704 | 0.8648 | 0.9764 |
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| 0.0343 | 2.0 | 1042 | 0.0870 | 0.8616 | 0.8757 | 0.8686 | 0.9781 |
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| 0.019 | 3.0 | 1563 | 0.0950 | 0.8599 | 0.8773 | 0.8685 | 0.9787 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.4.0
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- Datasets 2.20.0
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- Tokenizers 0.20.0
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