|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: facebook/bart-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- sacrebleu |
|
- rouge |
|
model-index: |
|
- name: bart-base-finetuned-w-data-augm-4e-5 |
|
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-base-finetuned-w-data-augm-4e-5 |
|
|
|
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3874 |
|
- Sacrebleu: 89.8161 |
|
- Rouge1: 95.6774 |
|
- Rouge2: 91.8937 |
|
- Rougel: 94.6649 |
|
- Rougelsum: 94.6595 |
|
- Bertscore Precision: 0.9414 |
|
- Bertscore Recall: 0.9376 |
|
- Bertscore F1: 0.9395 |
|
|
|
## 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: 4.4252514647201465e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:| |
|
| 0.1504 | 1.0 | 761 | 0.2797 | 90.9313 | 96.2421 | 92.8783 | 95.4262 | 95.4043 | 0.9496 | 0.9444 | 0.9469 | |
|
| 0.0348 | 2.0 | 1522 | 0.2473 | 91.7583 | 96.3865 | 93.2655 | 95.6899 | 95.6811 | 0.9532 | 0.9504 | 0.9517 | |
|
| 0.0587 | 3.0 | 2283 | 0.2413 | 91.828 | 96.4392 | 93.4124 | 95.7079 | 95.6976 | 0.9517 | 0.9508 | 0.9512 | |
|
| 0.0269 | 4.0 | 3044 | 0.2588 | 91.9835 | 96.578 | 93.6221 | 95.8992 | 95.8798 | 0.9524 | 0.9527 | 0.9525 | |
|
| 0.0439 | 5.0 | 3805 | 0.2678 | 92.1033 | 96.6815 | 93.6391 | 95.9677 | 95.9469 | 0.9544 | 0.9536 | 0.954 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
