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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: 0.2050 |
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- Precision: 0.6079 |
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- Recall: 0.5407 |
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- F1: 0.5723 |
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- Accuracy: 0.9528 |
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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: 12 |
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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 | 0.2223 | 0.3125 | 0.1619 | 0.2133 | 0.9212 | |
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| No log | 2.0 | 142 | 0.1599 | 0.5228 | 0.3539 | 0.4221 | 0.9446 | |
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| No log | 3.0 | 213 | 0.1472 | 0.5298 | 0.4385 | 0.4798 | 0.9470 | |
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| No log | 4.0 | 284 | 0.1441 | 0.5885 | 0.4729 | 0.5244 | 0.9514 | |
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| No log | 5.0 | 355 | 0.1675 | 0.5654 | 0.5146 | 0.5388 | 0.9491 | |
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| No log | 6.0 | 426 | 0.1592 | 0.5860 | 0.5082 | 0.5443 | 0.9521 | |
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| No log | 7.0 | 497 | 0.1634 | 0.5621 | 0.5587 | 0.5604 | 0.9509 | |
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| 0.1349 | 8.0 | 568 | 0.1897 | 0.5803 | 0.5182 | 0.5475 | 0.9515 | |
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| 0.1349 | 9.0 | 639 | 0.1880 | 0.5699 | 0.5539 | 0.5618 | 0.9506 | |
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| 0.1349 | 10.0 | 710 | 0.1939 | 0.5923 | 0.5415 | 0.5657 | 0.9525 | |
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| 0.1349 | 11.0 | 781 | 0.1988 | 0.5863 | 0.5475 | 0.5662 | 0.9518 | |
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| 0.1349 | 12.0 | 852 | 0.2050 | 0.6079 | 0.5407 | 0.5723 | 0.9528 | |
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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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