|
--- |
|
license: apache-2.0 |
|
base_model: sshleifer/distilbart-xsum-12-6 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: bart-abs-1509-0313-lr-0.0003-bs-4-maxep-10 |
|
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-abs-1509-0313-lr-0.0003-bs-4-maxep-10 |
|
|
|
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 4.7482 |
|
- Rouge/rouge1: 0.4015 |
|
- Rouge/rouge2: 0.1493 |
|
- Rouge/rougel: 0.329 |
|
- Rouge/rougelsum: 0.3294 |
|
- Bertscore/bertscore-precision: 0.8894 |
|
- Bertscore/bertscore-recall: 0.8807 |
|
- Bertscore/bertscore-f1: 0.8849 |
|
- Meteor: 0.3397 |
|
- Gen Len: 33.2 |
|
|
|
## 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: 0.0003 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- 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 | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:| |
|
| 1.722 | 1.0 | 217 | 2.6756 | 0.4274 | 0.1866 | 0.3588 | 0.3592 | 0.8936 | 0.884 | 0.8886 | 0.3527 | 32.9545 | |
|
| 1.7652 | 2.0 | 434 | 2.7321 | 0.416 | 0.1726 | 0.3511 | 0.3521 | 0.8944 | 0.8818 | 0.888 | 0.3352 | 31.5909 | |
|
| 1.135 | 3.0 | 651 | 2.9372 | 0.3752 | 0.1441 | 0.3163 | 0.3158 | 0.8968 | 0.8736 | 0.8849 | 0.2976 | 26.4 | |
|
| 0.9762 | 4.0 | 868 | 3.1311 | 0.3959 | 0.1535 | 0.3344 | 0.3353 | 0.8893 | 0.8777 | 0.8833 | 0.3296 | 33.1273 | |
|
| 0.7207 | 5.0 | 1085 | 3.3741 | 0.4028 | 0.1562 | 0.3388 | 0.3389 | 0.8889 | 0.8818 | 0.8852 | 0.3324 | 34.3273 | |
|
| 0.3986 | 6.0 | 1302 | 3.4504 | 0.4245 | 0.1689 | 0.3493 | 0.3501 | 0.892 | 0.8834 | 0.8876 | 0.351 | 34.4727 | |
|
| 0.2471 | 7.0 | 1519 | 3.8316 | 0.4096 | 0.1536 | 0.3384 | 0.3389 | 0.8922 | 0.8814 | 0.8867 | 0.3376 | 32.7909 | |
|
| 0.1613 | 8.0 | 1736 | 4.2439 | 0.4201 | 0.1621 | 0.346 | 0.347 | 0.8921 | 0.8815 | 0.8866 | 0.3503 | 33.3 | |
|
| 0.0989 | 9.0 | 1953 | 4.4784 | 0.4115 | 0.1499 | 0.3394 | 0.3408 | 0.8904 | 0.8825 | 0.8863 | 0.3409 | 34.0 | |
|
| 0.0644 | 10.0 | 2170 | 4.7482 | 0.4015 | 0.1493 | 0.329 | 0.3294 | 0.8894 | 0.8807 | 0.8849 | 0.3397 | 33.2 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.0 |
|
- Pytorch 2.4.0 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
