metadata
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-continued_training-medqa
results: []
distilbert-base-uncased-continued_training-medqa
This model is a fine-tuned version of Shaier/distilbert-base-uncased-continued_training-medqa on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4063
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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 333 | 0.4659 |
| No log | 2.0 | 666 | 0.4547 |
| No log | 3.0 | 999 | 0.3882 |
| No log | 4.0 | 1332 | 0.4310 |
| No log | 5.0 | 1665 | 0.4194 |
| No log | 6.0 | 1998 | 0.5209 |
| No log | 7.0 | 2331 | 0.4812 |
| 0.4829 | 8.0 | 2664 | 0.5321 |
| 0.4829 | 9.0 | 2997 | 0.3646 |
| 0.4829 | 10.0 | 3330 | 0.4339 |
| 0.4829 | 11.0 | 3663 | 0.5188 |
| 0.4829 | 12.0 | 3996 | 0.4148 |
| 0.4829 | 13.0 | 4329 | 0.4615 |
| 0.4829 | 14.0 | 4662 | 0.3825 |
| 0.4829 | 15.0 | 4995 | 0.4617 |
| 0.4773 | 16.0 | 5328 | 0.3400 |
| 0.4773 | 17.0 | 5661 | 0.4740 |
| 0.4773 | 18.0 | 5994 | 0.5057 |
| 0.4773 | 19.0 | 6327 | 0.5477 |
| 0.4773 | 20.0 | 6660 | 0.4426 |
| 0.4773 | 21.0 | 6993 | 0.3574 |
| 0.4773 | 22.0 | 7326 | 0.4031 |
| 0.4773 | 23.0 | 7659 | 0.4491 |
| 0.4715 | 24.0 | 7992 | 0.4340 |
| 0.4715 | 25.0 | 8325 | 0.4602 |
| 0.4715 | 26.0 | 8658 | 0.4659 |
| 0.4715 | 27.0 | 8991 | 0.4321 |
| 0.4715 | 28.0 | 9324 | 0.4335 |
| 0.4715 | 29.0 | 9657 | 0.4458 |
| 0.4715 | 30.0 | 9990 | 0.4285 |
| 0.4715 | 31.0 | 10323 | 0.5002 |
| 0.4671 | 32.0 | 10656 | 0.4706 |
| 0.4671 | 33.0 | 10989 | 0.5368 |
| 0.4671 | 34.0 | 11322 | 0.4028 |
| 0.4671 | 35.0 | 11655 | 0.5171 |
| 0.4671 | 36.0 | 11988 | 0.4506 |
| 0.4671 | 37.0 | 12321 | 0.4163 |
| 0.4671 | 38.0 | 12654 | 0.4905 |
| 0.4671 | 39.0 | 12987 | 0.5168 |
| 0.4646 | 40.0 | 13320 | 0.4412 |
| 0.4646 | 41.0 | 13653 | 0.4773 |
| 0.4646 | 42.0 | 13986 | 0.4835 |
| 0.4646 | 43.0 | 14319 | 0.4716 |
| 0.4646 | 44.0 | 14652 | 0.4431 |
| 0.4646 | 45.0 | 14985 | 0.4187 |
| 0.4646 | 46.0 | 15318 | 0.3389 |
| 0.4646 | 47.0 | 15651 | 0.4699 |
| 0.4628 | 48.0 | 15984 | 0.4880 |
| 0.4628 | 49.0 | 16317 | 0.5058 |
| 0.4628 | 50.0 | 16650 | 0.4275 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.3.2
- Tokenizers 0.11.0