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--- |
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library_name: transformers |
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base_model: dccuchile/bert-base-spanish-wwm-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- biobert_json |
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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: NER-finetuning-BETO-UNCASED-BIOBERT |
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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: biobert_json |
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type: biobert_json |
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config: Biobert_json |
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split: validation |
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args: Biobert_json |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9467966573816156 |
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- name: Recall |
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type: recall |
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value: 0.9626168224299065 |
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- name: F1 |
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type: f1 |
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value: 0.9546412020783598 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9762832612799123 |
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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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# NER-finetuning-BETO-UNCASED-BIOBERT |
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the biobert_json dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1165 |
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- Precision: 0.9468 |
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- Recall: 0.9626 |
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- F1: 0.9546 |
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- Accuracy: 0.9763 |
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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: 10 |
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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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| 0.3684 | 1.0 | 612 | 0.1093 | 0.9356 | 0.9523 | 0.9438 | 0.9708 | |
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| 0.1191 | 2.0 | 1224 | 0.1026 | 0.9388 | 0.9674 | 0.9529 | 0.9746 | |
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| 0.0842 | 3.0 | 1836 | 0.1011 | 0.9394 | 0.9690 | 0.9539 | 0.9754 | |
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| 0.0657 | 4.0 | 2448 | 0.0986 | 0.9468 | 0.9682 | 0.9574 | 0.9779 | |
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| 0.0437 | 5.0 | 3060 | 0.0988 | 0.9499 | 0.9649 | 0.9573 | 0.9772 | |
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| 0.0395 | 6.0 | 3672 | 0.1070 | 0.9446 | 0.9645 | 0.9545 | 0.9757 | |
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| 0.0311 | 7.0 | 4284 | 0.1110 | 0.9459 | 0.9673 | 0.9565 | 0.9766 | |
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| 0.0302 | 8.0 | 4896 | 0.1141 | 0.9449 | 0.9635 | 0.9541 | 0.9763 | |
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| 0.023 | 9.0 | 5508 | 0.1133 | 0.9485 | 0.9641 | 0.9563 | 0.9770 | |
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| 0.0198 | 10.0 | 6120 | 0.1165 | 0.9468 | 0.9626 | 0.9546 | 0.9763 | |
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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 3.0.1 |
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- Tokenizers 0.20.0 |
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