|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: t5-small-machine-articles-tag-generation |
|
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. --> |
|
|
|
# t5-small-machine-articles-tag-generation |
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.9833 |
|
- Rouge1: 35.3543 |
|
- Rouge2: 18.1226 |
|
- Rougel: 31.3958 |
|
- Rougelsum: 31.414 |
|
- Gen Len: 17.6596 |
|
|
|
## 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: 20 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| 3.7917 | 1.0 | 47 | 3.0002 | 19.9138 | 6.9215 | 17.6969 | 17.7888 | 18.9787 | |
|
| 3.0113 | 2.0 | 94 | 2.5823 | 22.9993 | 9.0341 | 20.8118 | 20.7657 | 18.7021 | |
|
| 2.7086 | 3.0 | 141 | 2.3643 | 26.7716 | 12.2207 | 24.1983 | 24.2611 | 18.3298 | |
|
| 2.5192 | 4.0 | 188 | 2.2361 | 28.5866 | 13.6305 | 26.1201 | 26.1367 | 17.9894 | |
|
| 2.4089 | 5.0 | 235 | 2.1661 | 30.1919 | 13.8779 | 27.1523 | 27.1256 | 18.0638 | |
|
| 2.3293 | 6.0 | 282 | 2.1185 | 31.1222 | 15.6736 | 27.3953 | 27.4457 | 17.8404 | |
|
| 2.2635 | 7.0 | 329 | 2.0875 | 32.3166 | 16.3032 | 28.7062 | 28.732 | 17.9149 | |
|
| 2.2349 | 8.0 | 376 | 2.0653 | 31.8387 | 15.616 | 28.3254 | 28.4288 | 17.7979 | |
|
| 2.1945 | 9.0 | 423 | 2.0473 | 32.388 | 16.4027 | 28.5642 | 28.6096 | 17.6809 | |
|
| 2.1658 | 10.0 | 470 | 2.0352 | 33.9489 | 16.999 | 29.8446 | 29.8251 | 17.5426 | |
|
| 2.1414 | 11.0 | 517 | 2.0252 | 34.0804 | 17.6999 | 30.1921 | 30.2739 | 17.5106 | |
|
| 2.1103 | 12.0 | 564 | 2.0155 | 34.3488 | 17.8273 | 30.2613 | 30.3358 | 17.5957 | |
|
| 2.1052 | 13.0 | 611 | 2.0053 | 35.1038 | 18.3494 | 30.6999 | 30.7655 | 17.6064 | |
|
| 2.0795 | 14.0 | 658 | 2.0004 | 35.366 | 18.8791 | 31.4931 | 31.5691 | 17.7872 | |
|
| 2.0612 | 15.0 | 705 | 1.9951 | 36.1778 | 18.7911 | 31.5974 | 31.6309 | 17.6064 | |
|
| 2.0792 | 16.0 | 752 | 1.9886 | 35.0387 | 18.2363 | 31.5279 | 31.5694 | 17.6702 | |
|
| 2.0695 | 17.0 | 799 | 1.9868 | 36.1432 | 18.4902 | 31.8314 | 31.7955 | 17.617 | |
|
| 2.0593 | 18.0 | 846 | 1.9844 | 35.7847 | 18.3497 | 31.745 | 31.7007 | 17.6809 | |
|
| 2.0395 | 19.0 | 893 | 1.9842 | 36.0629 | 18.9649 | 32.098 | 32.0453 | 17.5745 | |
|
| 2.0623 | 20.0 | 940 | 1.9833 | 35.3543 | 18.1226 | 31.3958 | 31.414 | 17.6596 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.1 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.9.0 |
|
- Tokenizers 0.13.2 |
|
|
