# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("austin/medberta_v2")
model = AutoModelForMaskedLM.from_pretrained("austin/medberta_v2")Quick Links
medberta_v2
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7111
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: 7e-05
- train_batch_size: 72
- eval_batch_size: 72
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9168 | 0.96 | 40000 | 0.8666 |
| 0.8392 | 1.91 | 80000 | 0.7871 |
| 0.7867 | 2.87 | 120000 | 0.7432 |
| 0.7418 | 3.83 | 160000 | 0.7111 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.10.0+cu113
- Datasets 1.16.1
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="austin/medberta_v2")