|
--- |
|
license: apache-2.0 |
|
base_model: facebook/bart-base |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: pubmed-mixed-noise-v3-0.2 |
|
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. --> |
|
|
|
# pubmed-mixed-noise-v3-0.2 |
|
|
|
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.4140 |
|
|
|
## 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: 3 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:-----:|:---------------:| |
|
| 0.7328 | 0.11 | 500 | 0.6952 | |
|
| 0.6994 | 0.21 | 1000 | 0.6005 | |
|
| 0.6684 | 0.32 | 1500 | 0.5670 | |
|
| 0.6305 | 0.43 | 2000 | 0.5402 | |
|
| 0.6224 | 0.54 | 2500 | 0.5175 | |
|
| 0.6009 | 0.64 | 3000 | 0.5001 | |
|
| 0.5126 | 0.75 | 3500 | 0.4935 | |
|
| 0.5698 | 0.86 | 4000 | 0.4793 | |
|
| 0.497 | 0.96 | 4500 | 0.4715 | |
|
| 0.3716 | 1.07 | 5000 | 0.4689 | |
|
| 0.4522 | 1.18 | 5500 | 0.4551 | |
|
| 0.3958 | 1.28 | 6000 | 0.4556 | |
|
| 0.4398 | 1.39 | 6500 | 0.4502 | |
|
| 0.4572 | 1.5 | 7000 | 0.4425 | |
|
| 0.4339 | 1.61 | 7500 | 0.4424 | |
|
| 0.4289 | 1.71 | 8000 | 0.4322 | |
|
| 0.3771 | 1.82 | 8500 | 0.4337 | |
|
| 0.3668 | 1.93 | 9000 | 0.4265 | |
|
| 0.3342 | 2.03 | 9500 | 0.4316 | |
|
| 0.3465 | 2.14 | 10000 | 0.4244 | |
|
| 0.32 | 2.25 | 10500 | 0.4226 | |
|
| 0.3493 | 2.35 | 11000 | 0.4244 | |
|
| 0.3549 | 2.46 | 11500 | 0.4216 | |
|
| 0.3281 | 2.57 | 12000 | 0.4192 | |
|
| 0.3259 | 2.68 | 12500 | 0.4181 | |
|
| 0.3444 | 2.78 | 13000 | 0.4156 | |
|
| 0.3201 | 2.89 | 13500 | 0.4146 | |
|
| 0.3315 | 3.0 | 14000 | 0.4140 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.1 |
|
- Pytorch 2.0.1 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|
