|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: bart-mlm-pubmed-15 |
|
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. --> |
|
|
|
# bart-mlm-pubmed-15 |
|
|
|
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: 0.4822 |
|
- Rouge2 Precision: 0.7578 |
|
- Rouge2 Recall: 0.5933 |
|
- Rouge2 Fmeasure: 0.6511 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
|
| 0.7006 | 1.0 | 663 | 0.5062 | 0.7492 | 0.5855 | 0.6434 | |
|
| 0.5709 | 2.0 | 1326 | 0.4811 | 0.7487 | 0.5879 | 0.6447 | |
|
| 0.5011 | 3.0 | 1989 | 0.4734 | 0.7541 | 0.5906 | 0.6483 | |
|
| 0.4164 | 4.0 | 2652 | 0.4705 | 0.7515 | 0.5876 | 0.6452 | |
|
| 0.3888 | 5.0 | 3315 | 0.4703 | 0.7555 | 0.5946 | 0.6515 | |
|
| 0.3655 | 6.0 | 3978 | 0.4725 | 0.7572 | 0.5943 | 0.6516 | |
|
| 0.319 | 7.0 | 4641 | 0.4733 | 0.7557 | 0.5911 | 0.6491 | |
|
| 0.3089 | 8.0 | 5304 | 0.4792 | 0.7577 | 0.5936 | 0.6513 | |
|
| 0.2907 | 9.0 | 5967 | 0.4799 | 0.7577 | 0.5931 | 0.6509 | |
|
| 0.275 | 10.0 | 6630 | 0.4822 | 0.7578 | 0.5933 | 0.6511 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.12.5 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.15.1 |
|
- Tokenizers 0.10.3 |
|
|
