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Austin-MeDeBERTa
austin/Austin-MeDeBERTa
Austin-MeDeBERTa is a fill-mask model from austin. Use it when you need the model to fill a missing word. It is set up for transformers. The card lists the license as mit.
This model was developed using further MLM pre-training on microsoft/deberta-base, using a dataset of 1.1M clinical notes from the Austin Health EMR. The notes span discharge summaries, inpatient notes, radiology repo…
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At a glance
- Task
- Fill-Mask
- Library
- transformers
- License
- mit
- Model type
- deberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Dec 8, 2021
- SHA
- 2197dd8a
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Model card
From the Hugging Face model README
Austin MeDeBERTa
This model was developed using further MLM pre-training on microsoft/deberta-base, using a dataset of 1.1M clinical notes from the Austin Health EMR. The notes span discharge summaries, inpatient notes, radiology reports and histopathology reports.
Model description
This is the base version of the original DeBERTa model. The architecture and tokenizer are unchanged.
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: 5e-05
- train_batch_size: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9756 | 0.51 | 40000 | 0.9127 |
| 0.8876 | 1.01 | 80000 | 0.8221 |
| 0.818 | 1.52 | 120000 | 0.7786 |
| 0.7836 | 2.03 | 160000 | 0.7438 |
| 0.7672 | 2.54 | 200000 | 0.7165 |
| 0.734 | 3.04 | 240000 | 0.6948 |
| 0.7079 | 3.55 | 280000 | 0.6749 |
| 0.6987 | 4.06 | 320000 | 0.6598 |
| 0.6771 | 4.57 | 360000 | 0.6471 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu113
- Datasets 1.15.1
- Tokenizers 0.10.3
- Task
- Fill-Mask
- Library
- transformers
- Type
- deberta
- License
- mit
- Created
- Mar 2, 2022
- Updated
- Dec 8, 2021