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README.md
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---
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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: biomed_roberta_all_deep
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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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# biomed_roberta_all_deep
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This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7519
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- Precision: 0.6732
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- Recall: 0.7142
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- F1: 0.6931
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- Accuracy: 0.8255
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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: 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 | 363 | 0.5600 | 0.6059 | 0.6773 | 0.6396 | 0.8131 |
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| 0.7102 | 2.0 | 726 | 0.5290 | 0.6310 | 0.7172 | 0.6713 | 0.8248 |
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| 0.4147 | 3.0 | 1089 | 0.5253 | 0.6620 | 0.7075 | 0.6840 | 0.8289 |
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| 0.4147 | 4.0 | 1452 | 0.5572 | 0.6664 | 0.7062 | 0.6857 | 0.8263 |
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| 0.3081 | 5.0 | 1815 | 0.5942 | 0.6615 | 0.7127 | 0.6862 | 0.8244 |
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| 0.231 | 6.0 | 2178 | 0.6393 | 0.6745 | 0.7064 | 0.6901 | 0.8268 |
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| 0.1864 | 7.0 | 2541 | 0.6771 | 0.6769 | 0.7050 | 0.6907 | 0.8250 |
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| 0.1864 | 8.0 | 2904 | 0.7091 | 0.6708 | 0.7120 | 0.6908 | 0.8263 |
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| 0.1523 | 9.0 | 3267 | 0.7463 | 0.6702 | 0.7159 | 0.6923 | 0.8255 |
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| 0.1336 | 10.0 | 3630 | 0.7519 | 0.6732 | 0.7142 | 0.6931 | 0.8255 |
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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