Training complete
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README.md
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---
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library_name: transformers
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base_model: allenai/biomed_roberta_base
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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: BioMedRoBERTa-finetuned-valid-testing-0.00005-16
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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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# BioMedRoBERTa-finetuned-valid-testing-0.00005-16
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This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0868
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- Precision: 0.8162
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- Recall: 0.8225
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- F1: 0.8194
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- Accuracy: 0.9766
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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: 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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 5
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- mixed_precision_training: Native AMP
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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 | 417 | 0.1006 | 0.7400 | 0.7857 | 0.7622 | 0.9694 |
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| 0.3728 | 2.0 | 834 | 0.0739 | 0.8268 | 0.8092 | 0.8179 | 0.9778 |
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| 0.0615 | 3.0 | 1251 | 0.0800 | 0.7988 | 0.8101 | 0.8044 | 0.9734 |
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| 0.0449 | 4.0 | 1668 | 0.0843 | 0.8111 | 0.8214 | 0.8162 | 0.9763 |
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| 0.0325 | 5.0 | 2085 | 0.0868 | 0.8162 | 0.8225 | 0.8194 | 0.9766 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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runs/Sep04_22-55-56_d7fd2e8d9a3c/events.out.tfevents.1725490557.d7fd2e8d9a3c.3166.4
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size
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size 9321
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