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---
base_model: allenai/biomed_roberta_base
tags:
- medical transcriptions
- healthcare
model-index:
- name: clinical_transcripts_roberta
results: []
widget:
- text: "General endotracheal <mask> was induced without incident. Preoperative antibiotics were given for prophylaxis against surgical infection."
---
<!-- 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. -->
# clinical_transcripts_roberta
This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on [medical transcriptions dataset](https://www.kaggle.com/datasets/tboyle10/medicaltranscriptions).
It achieves the following results on the evaluation set:
- Loss: 1.0331
## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.405 | 0.51 | 100 | 1.2925 |
| 1.316 | 1.01 | 200 | 1.2107 |
| 1.2781 | 1.52 | 300 | 1.1704 |
| 1.2911 | 2.02 | 400 | 1.1745 |
| 1.2241 | 2.53 | 500 | 1.1730 |
| 1.2063 | 3.03 | 600 | 1.1248 |
| 1.174 | 3.54 | 700 | 1.1416 |
| 1.1588 | 4.04 | 800 | 1.1495 |
| 1.1513 | 4.55 | 900 | 1.1145 |
| 1.1541 | 5.05 | 1000 | 1.1402 |
| 1.1266 | 5.56 | 1100 | 1.1156 |
| 1.1205 | 6.06 | 1200 | 1.1075 |
| 1.1141 | 6.57 | 1300 | 1.1157 |
| 1.0956 | 7.07 | 1400 | 1.1047 |
| 1.0809 | 7.58 | 1500 | 1.0921 |
| 1.0755 | 8.08 | 1600 | 1.0891 |
| 1.044 | 8.59 | 1700 | 1.0758 |
| 1.1103 | 9.09 | 1800 | 1.0881 |
| 1.0578 | 9.6 | 1900 | 1.0578 |
| 1.0462 | 10.1 | 2000 | 1.1043 |
| 1.0302 | 10.61 | 2100 | 1.0787 |
| 1.0236 | 11.11 | 2200 | 1.0841 |
| 1.0371 | 11.62 | 2300 | 1.0904 |
| 1.0178 | 12.12 | 2400 | 1.0593 |
| 0.999 | 12.63 | 2500 | 1.0661 |
| 0.9867 | 13.13 | 2600 | 1.0670 |
| 0.9986 | 13.64 | 2700 | 1.0470 |
| 0.9867 | 14.14 | 2800 | 1.0347 |
| 0.9848 | 14.65 | 2900 | 1.0274 |
| 0.9627 | 15.15 | 3000 | 1.0550 |
| 0.9659 | 15.66 | 3100 | 1.0499 |
| 0.9743 | 16.16 | 3200 | 1.0419 |
| 0.9507 | 16.67 | 3300 | 1.0679 |
| 0.941 | 17.17 | 3400 | 1.0142 |
| 0.9548 | 17.68 | 3500 | 1.0422 |
| 0.9378 | 18.18 | 3600 | 1.0471 |
| 0.9339 | 18.69 | 3700 | 1.0473 |
| 0.9195 | 19.19 | 3800 | 1.0248 |
| 0.9254 | 19.7 | 3900 | 1.0235 |
| 0.9393 | 20.2 | 4000 | 1.0331 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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