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license: mit |
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tags: |
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- generated_from_trainer |
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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: Yepes_2e-05_250 |
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results: [] |
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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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# Yepes_2e-05_250 |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1279 |
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- Precision: 0.6833 |
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- Recall: 0.5100 |
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- F1: 0.5840 |
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- Accuracy: 0.9788 |
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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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- training_steps: 500 |
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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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| 0.8467 | 1.39 | 25 | 0.2149 | 0.0 | 0.0 | 0.0 | 0.9672 | |
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| 0.1988 | 2.78 | 50 | 0.1959 | 0.0 | 0.0 | 0.0 | 0.9672 | |
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| 0.156 | 4.17 | 75 | 0.1439 | 0.3268 | 0.2065 | 0.2530 | 0.9691 | |
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| 0.1128 | 5.56 | 100 | 0.1324 | 0.49 | 0.2438 | 0.3256 | 0.9730 | |
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| 0.0978 | 6.94 | 125 | 0.1222 | 0.4964 | 0.3433 | 0.4059 | 0.9747 | |
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| 0.0788 | 8.33 | 150 | 0.1154 | 0.5193 | 0.3682 | 0.4309 | 0.9760 | |
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| 0.067 | 9.72 | 175 | 0.1162 | 0.4711 | 0.3856 | 0.4241 | 0.9749 | |
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| 0.058 | 11.11 | 200 | 0.1236 | 0.5275 | 0.3582 | 0.4267 | 0.9761 | |
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| 0.0491 | 12.5 | 225 | 0.1177 | 0.4940 | 0.4104 | 0.4484 | 0.9754 | |
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| 0.0443 | 13.89 | 250 | 0.1235 | 0.5472 | 0.4179 | 0.4739 | 0.9767 | |
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| 0.0383 | 15.28 | 275 | 0.1198 | 0.5764 | 0.4502 | 0.5056 | 0.9770 | |
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| 0.0369 | 16.67 | 300 | 0.1219 | 0.5892 | 0.4602 | 0.5168 | 0.9776 | |
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| 0.0326 | 18.06 | 325 | 0.1261 | 0.7 | 0.4701 | 0.5625 | 0.9790 | |
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| 0.0305 | 19.44 | 350 | 0.1269 | 0.6904 | 0.4826 | 0.5681 | 0.9787 | |
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| 0.0269 | 20.83 | 375 | 0.1252 | 0.6656 | 0.5 | 0.5710 | 0.9783 | |
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| 0.025 | 22.22 | 400 | 0.1253 | 0.6529 | 0.5100 | 0.5726 | 0.9782 | |
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| 0.0244 | 23.61 | 425 | 0.1284 | 0.6875 | 0.4925 | 0.5739 | 0.9790 | |
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| 0.0224 | 25.0 | 450 | 0.1279 | 0.6833 | 0.5100 | 0.5840 | 0.9788 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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