---
base_model: allenai/biomed_roberta_base
tags:
- healthcare
- medical
- pharma
- surgery
model-index:
- name: delirium_roberta
  results: []

widget:
- 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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# delirium_roberta

This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base).
It achieves the following results on the evaluation set:
- Loss: 0.3709

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 100
- training_steps: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2088        | 0.4   | 100  | 0.8023          |
| 0.8075        | 0.8   | 200  | 0.7029          |
| 0.7404        | 1.2   | 300  | 0.6575          |
| 0.6826        | 1.6   | 400  | 0.6096          |
| 0.6578        | 2.0   | 500  | 0.5995          |
| 0.6525        | 2.4   | 600  | 0.5834          |
| 0.6223        | 2.8   | 700  | 0.5650          |
| 0.6           | 3.2   | 800  | 0.5464          |
| 0.5807        | 3.6   | 900  | 0.5312          |
| 0.5963        | 4.0   | 1000 | 0.5233          |
| 0.584         | 4.4   | 1100 | 0.5154          |
| 0.5508        | 4.8   | 1200 | 0.5049          |
| 0.5609        | 5.2   | 1300 | 0.4960          |
| 0.5397        | 5.6   | 1400 | 0.4851          |
| 0.5401        | 6.0   | 1500 | 0.4805          |
| 0.513         | 6.4   | 1600 | 0.4690          |
| 0.5247        | 6.8   | 1700 | 0.4647          |
| 0.5228        | 7.2   | 1800 | 0.4607          |
| 0.5142        | 7.6   | 1900 | 0.4534          |
| 0.5055        | 8.0   | 2000 | 0.4428          |
| 0.4942        | 8.4   | 2100 | 0.4338          |
| 0.4895        | 8.8   | 2200 | 0.4336          |
| 0.4874        | 9.2   | 2300 | 0.4221          |
| 0.4744        | 9.6   | 2400 | 0.4234          |
| 0.4743        | 10.0  | 2500 | 0.4139          |
| 0.4816        | 10.4  | 2600 | 0.4090          |
| 0.4733        | 10.8  | 2700 | 0.4077          |
| 0.4419        | 11.2  | 2800 | 0.4035          |
| 0.4552        | 11.6  | 2900 | 0.3989          |
| 0.4467        | 12.0  | 3000 | 0.3913          |
| 0.45          | 12.4  | 3100 | 0.3884          |
| 0.4551        | 12.8  | 3200 | 0.3864          |
| 0.4247        | 13.2  | 3300 | 0.3786          |
| 0.4432        | 13.6  | 3400 | 0.3874          |
| 0.4086        | 14.0  | 3500 | 0.3776          |
| 0.4308        | 14.4  | 3600 | 0.3711          |
| 0.4293        | 14.8  | 3700 | 0.3763          |
| 0.4235        | 15.2  | 3800 | 0.3733          |
| 0.4138        | 15.6  | 3900 | 0.3758          |
| 0.4156        | 16.0  | 4000 | 0.3709          |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1