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--- |
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base_model: allenai/biomed_roberta_base |
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tags: |
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- healthcare |
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- medical |
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- pharma |
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- surgery |
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model-index: |
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- name: delirium_roberta |
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results: [] |
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widget: |
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- text: The patient has a clinical history of herniated disc, glioblastoma operated on last year and will undergo temporal malignant neoplasty surgery. The patient's diagnosis is malignant <mask> of temporal lobe |
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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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# delirium_roberta |
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This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3709 |
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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: 0.0005 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 4000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.2088 | 0.4 | 100 | 0.8023 | |
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| 0.8075 | 0.8 | 200 | 0.7029 | |
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| 0.7404 | 1.2 | 300 | 0.6575 | |
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| 0.6826 | 1.6 | 400 | 0.6096 | |
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| 0.6578 | 2.0 | 500 | 0.5995 | |
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| 0.6525 | 2.4 | 600 | 0.5834 | |
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| 0.6223 | 2.8 | 700 | 0.5650 | |
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| 0.6 | 3.2 | 800 | 0.5464 | |
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| 0.5807 | 3.6 | 900 | 0.5312 | |
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| 0.5963 | 4.0 | 1000 | 0.5233 | |
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| 0.584 | 4.4 | 1100 | 0.5154 | |
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| 0.5508 | 4.8 | 1200 | 0.5049 | |
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| 0.5609 | 5.2 | 1300 | 0.4960 | |
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| 0.5397 | 5.6 | 1400 | 0.4851 | |
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| 0.5401 | 6.0 | 1500 | 0.4805 | |
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| 0.513 | 6.4 | 1600 | 0.4690 | |
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| 0.5247 | 6.8 | 1700 | 0.4647 | |
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| 0.5228 | 7.2 | 1800 | 0.4607 | |
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| 0.5142 | 7.6 | 1900 | 0.4534 | |
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| 0.5055 | 8.0 | 2000 | 0.4428 | |
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| 0.4942 | 8.4 | 2100 | 0.4338 | |
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| 0.4895 | 8.8 | 2200 | 0.4336 | |
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| 0.4874 | 9.2 | 2300 | 0.4221 | |
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| 0.4744 | 9.6 | 2400 | 0.4234 | |
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| 0.4743 | 10.0 | 2500 | 0.4139 | |
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| 0.4816 | 10.4 | 2600 | 0.4090 | |
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| 0.4733 | 10.8 | 2700 | 0.4077 | |
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| 0.4419 | 11.2 | 2800 | 0.4035 | |
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| 0.4552 | 11.6 | 2900 | 0.3989 | |
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| 0.4467 | 12.0 | 3000 | 0.3913 | |
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| 0.45 | 12.4 | 3100 | 0.3884 | |
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| 0.4551 | 12.8 | 3200 | 0.3864 | |
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| 0.4247 | 13.2 | 3300 | 0.3786 | |
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| 0.4432 | 13.6 | 3400 | 0.3874 | |
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| 0.4086 | 14.0 | 3500 | 0.3776 | |
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| 0.4308 | 14.4 | 3600 | 0.3711 | |
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| 0.4293 | 14.8 | 3700 | 0.3763 | |
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| 0.4235 | 15.2 | 3800 | 0.3733 | |
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| 0.4138 | 15.6 | 3900 | 0.3758 | |
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| 0.4156 | 16.0 | 4000 | 0.3709 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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