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--- |
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license: mit |
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base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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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: NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model |
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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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# NLP-HIBA_BiomedNLP-BiomedBERT-base-pretrained-model |
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This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-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: 0.1766 |
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- Precision: 0.5977 |
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- Recall: 0.5730 |
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- F1: 0.5851 |
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- Accuracy: 0.9539 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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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| No log | 1.0 | 71 | 0.1981 | 0.2581 | 0.2239 | 0.2398 | 0.9266 | |
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| No log | 2.0 | 142 | 0.1616 | 0.4514 | 0.3692 | 0.4062 | 0.9444 | |
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| No log | 3.0 | 213 | 0.1514 | 0.5233 | 0.4727 | 0.4967 | 0.9482 | |
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| No log | 4.0 | 284 | 0.1863 | 0.4522 | 0.5546 | 0.4982 | 0.9352 | |
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| No log | 5.0 | 355 | 0.1582 | 0.5665 | 0.5245 | 0.5447 | 0.9498 | |
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| No log | 6.0 | 426 | 0.1571 | 0.5915 | 0.5305 | 0.5593 | 0.9529 | |
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| No log | 7.0 | 497 | 0.1652 | 0.5849 | 0.5586 | 0.5714 | 0.9527 | |
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| 0.1311 | 8.0 | 568 | 0.1676 | 0.5858 | 0.5738 | 0.5798 | 0.9528 | |
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| 0.1311 | 9.0 | 639 | 0.1748 | 0.5990 | 0.5562 | 0.5768 | 0.9537 | |
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| 0.1311 | 10.0 | 710 | 0.1766 | 0.5977 | 0.5730 | 0.5851 | 0.9539 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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