---
license: mit
tags:
- generated_from_trainer
model-index:
- name: ec-biogpt-masked-pubmed
  results: []
---

<!-- 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. -->

# ec-biogpt-masked-pubmed

This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7418

## 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: 5e-05
- 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_steps: 10
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3707        | 0.07  | 500   | 0.8468          |
| 0.5388        | 0.14  | 1000  | 0.7643          |
| 0.5857        | 0.21  | 1500  | 0.7669          |
| 0.5441        | 0.28  | 2000  | 0.7576          |
| 0.5294        | 0.36  | 2500  | 0.7570          |
| 0.7544        | 0.43  | 3000  | 0.7227          |
| 0.7075        | 0.5   | 3500  | 0.7153          |
| 0.7513        | 0.57  | 4000  | 0.7105          |
| 0.7101        | 0.64  | 4500  | 0.7059          |
| 0.7369        | 0.71  | 5000  | 0.7031          |
| 0.7477        | 0.78  | 5500  | 0.6991          |
| 0.6831        | 0.85  | 6000  | 0.6978          |
| 0.6458        | 0.93  | 6500  | 0.6940          |
| 0.6998        | 1.0   | 7000  | 0.6907          |
| 0.5901        | 1.07  | 7500  | 0.7036          |
| 0.633         | 1.14  | 8000  | 0.7016          |
| 0.6375        | 1.21  | 8500  | 0.7020          |
| 0.6378        | 1.28  | 9000  | 0.6988          |
| 0.5952        | 1.35  | 9500  | 0.6965          |
| 0.5714        | 1.42  | 10000 | 0.6960          |
| 0.5874        | 1.5   | 10500 | 0.6957          |
| 0.5828        | 1.57  | 11000 | 0.6917          |
| 0.5921        | 1.64  | 11500 | 0.6920          |
| 0.6086        | 1.71  | 12000 | 0.6905          |
| 0.5872        | 1.78  | 12500 | 0.6878          |
| 0.5895        | 1.85  | 13000 | 0.6883          |
| 0.5953        | 1.92  | 13500 | 0.6860          |
| 0.598         | 1.99  | 14000 | 0.6852          |
| 0.4805        | 2.07  | 14500 | 0.7077          |
| 0.4885        | 2.14  | 15000 | 0.7107          |
| 0.5048        | 2.21  | 15500 | 0.7083          |
| 0.4665        | 2.28  | 16000 | 0.7098          |
| 0.5057        | 2.35  | 16500 | 0.7088          |
| 0.4706        | 2.42  | 17000 | 0.7081          |
| 0.5056        | 2.49  | 17500 | 0.7076          |
| 0.4884        | 2.56  | 18000 | 0.7068          |
| 0.487         | 2.64  | 18500 | 0.7051          |
| 0.5327        | 2.71  | 19000 | 0.7062          |
| 0.4902        | 2.78  | 19500 | 0.7042          |
| 0.5277        | 2.85  | 20000 | 0.7021          |
| 0.499         | 2.92  | 20500 | 0.7024          |
| 0.4981        | 2.99  | 21000 | 0.7002          |
| 0.4174        | 3.06  | 21500 | 0.7237          |
| 0.4233        | 3.13  | 22000 | 0.7244          |
| 0.4331        | 3.21  | 22500 | 0.7265          |
| 0.4203        | 3.28  | 23000 | 0.7275          |
| 0.4265        | 3.35  | 23500 | 0.7252          |
| 0.4302        | 3.42  | 24000 | 0.7271          |
| 0.4343        | 3.49  | 24500 | 0.7244          |
| 0.4264        | 3.56  | 25000 | 0.7265          |
| 0.4565        | 3.63  | 25500 | 0.7247          |
| 0.4258        | 3.7   | 26000 | 0.7245          |
| 0.4191        | 3.78  | 26500 | 0.7246          |
| 0.4412        | 3.85  | 27000 | 0.7234          |
| 0.4604        | 3.92  | 27500 | 0.7249          |
| 0.4197        | 3.99  | 28000 | 0.7238          |
| 0.3666        | 4.06  | 28500 | 0.7413          |
| 0.3772        | 4.13  | 29000 | 0.7414          |
| 0.3628        | 4.2   | 29500 | 0.7410          |
| 0.3611        | 4.27  | 30000 | 0.7431          |
| 0.3736        | 4.35  | 30500 | 0.7414          |
| 0.3741        | 4.42  | 31000 | 0.7420          |
| 0.3661        | 4.49  | 31500 | 0.7424          |
| 0.3966        | 4.56  | 32000 | 0.7423          |
| 0.4058        | 4.63  | 32500 | 0.7423          |
| 0.4028        | 4.7   | 33000 | 0.7423          |
| 0.4028        | 4.77  | 33500 | 0.7420          |
| 0.3802        | 4.84  | 34000 | 0.7421          |
| 0.3612        | 4.92  | 34500 | 0.7418          |
| 0.3804        | 4.99  | 35000 | 0.7418          |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2