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--- |
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license: apache-2.0 |
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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: Plant-gob-roberta-base-bne-capitel-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: 0.8627583108715184 |
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- name: Recall |
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type: recall |
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value: 0.8825827205882353 |
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- name: F1 |
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type: f1 |
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value: 0.872557928214448 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9784878927600843 |
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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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# Plant-gob-roberta-base-bne-capitel-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: 0.1071 |
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- Precision: 0.8628 |
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- Recall: 0.8826 |
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- F1: 0.8726 |
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- Accuracy: 0.9785 |
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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: 5 |
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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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| 0.0681 | 1.0 | 521 | 0.0818 | 0.8645 | 0.8842 | 0.8742 | 0.9789 | |
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| 0.0313 | 2.0 | 1042 | 0.0841 | 0.8516 | 0.8768 | 0.8640 | 0.9779 | |
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| 0.0179 | 3.0 | 1563 | 0.0979 | 0.8584 | 0.8773 | 0.8677 | 0.9781 | |
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| 0.0091 | 4.0 | 2084 | 0.0996 | 0.8600 | 0.8819 | 0.8708 | 0.9784 | |
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| 0.0068 | 5.0 | 2605 | 0.1071 | 0.8628 | 0.8826 | 0.8726 | 0.9785 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
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