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delirium_roberta
This model is a fine-tuned version of 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
