|
--- |
|
license: apache-2.0 |
|
base_model: t5-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: T5_base_title_v2 |
|
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_base_title_v2 |
|
|
|
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.0995 |
|
- Rouge1: 0.3574 |
|
- Rouge2: 0.1666 |
|
- Rougel: 0.3037 |
|
- Rougelsum: 0.303 |
|
- Gen Len: 16.495 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
|
| No log | 1.0 | 100 | 2.1695 | 0.3249 | 0.1495 | 0.2795 | 0.2798 | 17.315 | |
|
| No log | 2.0 | 200 | 2.0994 | 0.3595 | 0.1696 | 0.3078 | 0.3085 | 16.825 | |
|
| No log | 3.0 | 300 | 2.0724 | 0.3679 | 0.1836 | 0.312 | 0.3131 | 16.525 | |
|
| No log | 4.0 | 400 | 2.0745 | 0.3669 | 0.1767 | 0.3137 | 0.3141 | 16.505 | |
|
| 2.0908 | 5.0 | 500 | 2.0567 | 0.3725 | 0.181 | 0.3205 | 0.3211 | 16.545 | |
|
| 2.0908 | 6.0 | 600 | 2.0575 | 0.3654 | 0.174 | 0.3101 | 0.3097 | 16.62 | |
|
| 2.0908 | 7.0 | 700 | 2.0640 | 0.3475 | 0.1649 | 0.2959 | 0.2956 | 16.485 | |
|
| 2.0908 | 8.0 | 800 | 2.0588 | 0.3678 | 0.1827 | 0.312 | 0.3113 | 16.54 | |
|
| 2.0908 | 9.0 | 900 | 2.0615 | 0.3654 | 0.1774 | 0.3106 | 0.3098 | 16.565 | |
|
| 1.696 | 10.0 | 1000 | 2.0689 | 0.3654 | 0.1767 | 0.3077 | 0.3069 | 16.78 | |
|
| 1.696 | 11.0 | 1100 | 2.0767 | 0.3633 | 0.1736 | 0.309 | 0.3078 | 16.57 | |
|
| 1.696 | 12.0 | 1200 | 2.0749 | 0.366 | 0.1802 | 0.3147 | 0.3145 | 16.755 | |
|
| 1.696 | 13.0 | 1300 | 2.0782 | 0.3632 | 0.1714 | 0.3117 | 0.3111 | 16.95 | |
|
| 1.696 | 14.0 | 1400 | 2.0841 | 0.3637 | 0.1718 | 0.3118 | 0.3111 | 16.855 | |
|
| 1.5311 | 15.0 | 1500 | 2.0873 | 0.3618 | 0.1713 | 0.3073 | 0.307 | 16.57 | |
|
| 1.5311 | 16.0 | 1600 | 2.0940 | 0.3655 | 0.1714 | 0.3115 | 0.3111 | 16.625 | |
|
| 1.5311 | 17.0 | 1700 | 2.0943 | 0.3619 | 0.1683 | 0.3089 | 0.3082 | 16.525 | |
|
| 1.5311 | 18.0 | 1800 | 2.0981 | 0.3609 | 0.1697 | 0.3074 | 0.3065 | 16.44 | |
|
| 1.5311 | 19.0 | 1900 | 2.0990 | 0.3567 | 0.1665 | 0.3047 | 0.3036 | 16.47 | |
|
| 1.447 | 20.0 | 2000 | 2.0995 | 0.3574 | 0.1666 | 0.3037 | 0.303 | 16.495 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.2 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.15.1 |
|
|
