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---
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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: bert-base-parsbert-uncased-ncbi_disease
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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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# bert-base-parsbert-uncased-ncbi_disease
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This model is a fine-tuned version of [HooshvareLab/bert-base-parsbert-uncased](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1018
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- Precision: 0.8192
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- Recall: 0.8645
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- F1: 0.8412
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- Accuracy: 0.9862
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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: 32
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- eval_batch_size: 32
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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: 15
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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 | 169 | 0.0648 | 0.7154 | 0.8237 | 0.7657 | 0.9813 |
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| No log | 2.0 | 338 | 0.0573 | 0.7870 | 0.8263 | 0.8062 | 0.9853 |
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| 0.0596 | 3.0 | 507 | 0.0639 | 0.7893 | 0.8776 | 0.8312 | 0.9858 |
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| 0.0596 | 4.0 | 676 | 0.0678 | 0.8150 | 0.8461 | 0.8302 | 0.9860 |
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| 0.0596 | 5.0 | 845 | 0.0737 | 0.8070 | 0.8474 | 0.8267 | 0.9862 |
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| 0.0065 | 6.0 | 1014 | 0.0834 | 0.8052 | 0.8592 | 0.8313 | 0.9856 |
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| 0.0065 | 7.0 | 1183 | 0.0918 | 0.8099 | 0.8355 | 0.8225 | 0.9859 |
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| 0.0065 | 8.0 | 1352 | 0.0882 | 0.8061 | 0.8697 | 0.8367 | 0.9857 |
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| 0.0021 | 9.0 | 1521 | 0.0903 | 0.8045 | 0.85 | 0.8266 | 0.9860 |
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| 0.0021 | 10.0 | 1690 | 0.0965 | 0.8303 | 0.85 | 0.8401 | 0.9866 |
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| 0.0021 | 11.0 | 1859 | 0.0954 | 0.8182 | 0.8645 | 0.8407 | 0.9860 |
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| 0.0008 | 12.0 | 2028 | 0.0998 | 0.8206 | 0.8605 | 0.8401 | 0.9862 |
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| 0.0008 | 13.0 | 2197 | 0.0995 | 0.82 | 0.8632 | 0.8410 | 0.9862 |
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| 0.0008 | 14.0 | 2366 | 0.1015 | 0.8214 | 0.8592 | 0.8399 | 0.9861 |
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| 0.0004 | 15.0 | 2535 | 0.1018 | 0.8192 | 0.8645 | 0.8412 | 0.9862 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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