|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- xsum |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: t5-small_adafactor |
|
results: |
|
- task: |
|
name: Sequence-to-sequence Language Modeling |
|
type: text2text-generation |
|
dataset: |
|
name: xsum |
|
type: xsum |
|
args: default |
|
metrics: |
|
- name: Rouge1 |
|
type: rouge |
|
value: 32.3784 |
|
--- |
|
|
|
<!-- 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_adafactor |
|
|
|
This model was trained from scratch on the xsum dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.1513 |
|
- Rouge1: 32.3784 |
|
- Rouge2: 11.2335 |
|
- Rougel: 26.1197 |
|
- Rougelsum: 26.1212 |
|
- Gen Len: 18.8066 |
|
|
|
## 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: 0.001 |
|
- train_batch_size: 24 |
|
- eval_batch_size: 24 |
|
- seed: 42 |
|
- optimizer: Adafactor |
|
- lr_scheduler_type: linear |
|
- num_epochs: 1 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
|
| 2.4206 | 0.02 | 200 | 2.2951 | 30.6414 | 9.9248 | 24.5953 | 24.6021 | 18.7814 | |
|
| 2.4363 | 0.05 | 400 | 2.3041 | 30.969 | 9.9594 | 24.9531 | 24.9484 | 18.7812 | |
|
| 2.4442 | 0.07 | 600 | 2.3042 | 30.9605 | 9.8821 | 24.9273 | 24.9343 | 18.787 | |
|
| 2.4402 | 0.09 | 800 | 2.2985 | 31.1667 | 9.9976 | 25.034 | 25.0346 | 18.7505 | |
|
| 2.4394 | 0.12 | 1000 | 2.2951 | 30.8935 | 9.8125 | 24.8084 | 24.8066 | 18.878 | |
|
| 2.4148 | 0.14 | 1200 | 2.2965 | 31.4419 | 10.1935 | 25.1234 | 25.1165 | 18.8134 | |
|
| 2.4329 | 0.16 | 1400 | 2.2891 | 30.735 | 9.7912 | 24.6127 | 24.6084 | 18.7797 | |
|
| 2.4308 | 0.19 | 1600 | 2.2950 | 31.0388 | 10.13 | 24.9166 | 24.9086 | 18.8409 | |
|
| 2.4302 | 0.21 | 1800 | 2.2808 | 30.978 | 10.0544 | 24.9191 | 24.9158 | 18.8147 | |
|
| 2.4165 | 0.24 | 2000 | 2.2785 | 31.2423 | 10.2329 | 25.2027 | 25.192 | 18.7531 | |
|
| 2.4227 | 0.26 | 2200 | 2.2705 | 30.8977 | 10.0552 | 24.8875 | 24.8869 | 18.8472 | |
|
| 2.4117 | 0.28 | 2400 | 2.2691 | 30.9478 | 10.1551 | 24.8565 | 24.8527 | 18.8049 | |
|
| 2.4229 | 0.31 | 2600 | 2.2635 | 31.1634 | 10.2055 | 25.0868 | 25.084 | 18.8424 | |
|
| 2.4163 | 0.33 | 2800 | 2.2554 | 31.2877 | 10.4018 | 25.2972 | 25.2924 | 18.8127 | |
|
| 2.4109 | 0.35 | 3000 | 2.2498 | 31.5192 | 10.3888 | 25.3461 | 25.3489 | 18.8066 | |
|
| 2.3883 | 0.38 | 3200 | 2.2473 | 31.4033 | 10.3393 | 25.2324 | 25.2297 | 18.8657 | |
|
| 2.3946 | 0.4 | 3400 | 2.2443 | 31.9869 | 10.7348 | 25.7509 | 25.7521 | 18.7703 | |
|
| 2.3726 | 0.42 | 3600 | 2.2398 | 31.6649 | 10.4532 | 25.4268 | 25.4221 | 18.8244 | |
|
| 2.3949 | 0.45 | 3800 | 2.2335 | 31.7186 | 10.6587 | 25.5281 | 25.5234 | 18.7766 | |
|
| 2.387 | 0.47 | 4000 | 2.2267 | 32.015 | 10.7906 | 25.7612 | 25.7634 | 18.7552 | |
|
| 2.3737 | 0.49 | 4200 | 2.2262 | 31.7823 | 10.7758 | 25.6306 | 25.6343 | 18.7436 | |
|
| 2.37 | 0.52 | 4400 | 2.2238 | 31.5111 | 10.6443 | 25.3768 | 25.3782 | 18.7801 | |
|
| 2.3748 | 0.54 | 4600 | 2.2166 | 31.6585 | 10.5958 | 25.4283 | 25.4321 | 18.7989 | |
|
| 2.3789 | 0.56 | 4800 | 2.2100 | 31.829 | 10.7779 | 25.6561 | 25.648 | 18.7688 | |
|
| 2.3659 | 0.59 | 5000 | 2.2064 | 32.0499 | 10.9069 | 25.8784 | 25.8725 | 18.8464 | |
|
| 2.3656 | 0.61 | 5200 | 2.2032 | 31.8874 | 10.7972 | 25.6996 | 25.6948 | 18.75 | |
|
| 2.3593 | 0.64 | 5400 | 2.1987 | 31.9182 | 10.7176 | 25.672 | 25.6662 | 18.8595 | |
|
| 2.3445 | 0.66 | 5600 | 2.1935 | 31.9871 | 10.803 | 25.7289 | 25.7247 | 18.7972 | |
|
| 2.3439 | 0.68 | 5800 | 2.1870 | 32.1788 | 10.9332 | 25.9597 | 25.9605 | 18.8062 | |
|
| 2.3489 | 0.71 | 6000 | 2.1845 | 32.0946 | 10.9864 | 25.9296 | 25.9342 | 18.8307 | |
|
| 2.3759 | 0.73 | 6200 | 2.1796 | 32.3321 | 11.0971 | 26.084 | 26.0843 | 18.7956 | |
|
| 2.3611 | 0.75 | 6400 | 2.1759 | 32.0703 | 10.8886 | 25.8437 | 25.8369 | 18.7629 | |
|
| 2.3319 | 0.78 | 6600 | 2.1722 | 31.8674 | 10.8993 | 25.6791 | 25.686 | 18.8292 | |
|
| 2.3445 | 0.8 | 6800 | 2.1686 | 32.1679 | 11.0594 | 25.8591 | 25.8604 | 18.817 | |
|
| 2.3523 | 0.82 | 7000 | 2.1667 | 32.2232 | 11.1537 | 25.9326 | 25.9359 | 18.8073 | |
|
| 2.3439 | 0.85 | 7200 | 2.1641 | 32.246 | 11.1854 | 26.015 | 26.0097 | 18.7954 | |
|
| 2.3496 | 0.87 | 7400 | 2.1603 | 32.1141 | 11.0758 | 25.9561 | 25.9623 | 18.7639 | |
|
| 2.3368 | 0.89 | 7600 | 2.1580 | 32.3447 | 11.1661 | 26.0906 | 26.0888 | 18.7936 | |
|
| 2.3634 | 0.92 | 7800 | 2.1553 | 32.3039 | 11.2246 | 26.0819 | 26.0828 | 18.7922 | |
|
| 2.3396 | 0.94 | 8000 | 2.1534 | 32.2979 | 11.262 | 26.0726 | 26.071 | 18.8069 | |
|
| 2.3645 | 0.96 | 8200 | 2.1520 | 32.4169 | 11.292 | 26.1811 | 26.187 | 18.7921 | |
|
| 2.341 | 0.99 | 8400 | 2.1513 | 32.3784 | 11.2335 | 26.1197 | 26.1212 | 18.8066 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.12.0+cu113 |
|
- Datasets 2.3.2 |
|
- Tokenizers 0.12.1 |
|
|
