File size: 4,161 Bytes
80b4b83 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 26.7383
---
<!-- 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-finetuned-wikihow_3epoch_b4_lr3e-3
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3400
- Rouge1: 26.7383
- Rouge2: 10.1981
- Rougel: 22.8642
- Rougelsum: 26.0922
- Gen Len: 18.524
## 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: 0.003
- 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.2548 | 0.13 | 5000 | 2.9708 | 22.0519 | 6.7142 | 18.7677 | 21.4627 | 17.9546 |
| 3.1153 | 0.25 | 10000 | 2.9099 | 20.2838 | 5.8365 | 17.5009 | 19.7112 | 18.4981 |
| 3.0478 | 0.38 | 15000 | 2.8763 | 22.8282 | 7.3649 | 19.6843 | 22.2312 | 18.1331 |
| 3.0146 | 0.51 | 20000 | 2.8484 | 23.2465 | 7.4295 | 19.621 | 22.6246 | 18.5115 |
| 2.9572 | 0.64 | 25000 | 2.7902 | 23.8681 | 7.9617 | 20.4984 | 23.2066 | 18.5544 |
| 2.9425 | 0.76 | 30000 | 2.7577 | 23.4402 | 7.5289 | 19.7382 | 22.7941 | 18.4613 |
| 2.9075 | 0.89 | 35000 | 2.7343 | 23.0082 | 7.5408 | 19.8426 | 22.3832 | 18.1218 |
| 2.8705 | 1.02 | 40000 | 2.7136 | 23.9492 | 7.8861 | 20.3675 | 23.3035 | 18.4869 |
| 2.7967 | 1.14 | 45000 | 2.6923 | 24.2394 | 8.2895 | 20.7275 | 23.6127 | 18.3486 |
| 2.7794 | 1.27 | 50000 | 2.6639 | 24.4062 | 8.2481 | 20.8957 | 23.8077 | 18.4258 |
| 2.7776 | 1.4 | 55000 | 2.6321 | 24.6213 | 8.4161 | 21.0528 | 23.968 | 18.351 |
| 2.7397 | 1.53 | 60000 | 2.6116 | 24.16 | 8.3605 | 20.618 | 23.5037 | 18.6049 |
| 2.7199 | 1.65 | 65000 | 2.5846 | 24.2606 | 8.3829 | 20.6274 | 23.6252 | 18.4742 |
| 2.7044 | 1.78 | 70000 | 2.5663 | 25.0452 | 8.896 | 21.4554 | 24.4748 | 18.3143 |
| 2.6928 | 1.91 | 75000 | 2.5365 | 25.1312 | 9.008 | 21.6376 | 24.4963 | 18.5605 |
| 2.6281 | 2.03 | 80000 | 2.5209 | 25.5311 | 9.1521 | 21.729 | 24.8864 | 18.2597 |
| 2.5333 | 2.16 | 85000 | 2.4860 | 25.4834 | 9.2969 | 21.7257 | 24.8802 | 18.3831 |
| 2.5308 | 2.29 | 90000 | 2.4619 | 26.0526 | 9.605 | 22.2178 | 25.4353 | 18.4235 |
| 2.5136 | 2.42 | 95000 | 2.4356 | 25.9434 | 9.6537 | 22.2957 | 25.312 | 18.4647 |
| 2.4801 | 2.54 | 100000 | 2.4098 | 26.1109 | 9.7637 | 22.3844 | 25.4771 | 18.5765 |
| 2.4494 | 2.67 | 105000 | 2.3835 | 26.332 | 9.9472 | 22.4243 | 25.6933 | 18.5985 |
| 2.4393 | 2.8 | 110000 | 2.3590 | 26.6896 | 10.2248 | 22.8743 | 26.0665 | 18.4883 |
| 2.4071 | 2.93 | 115000 | 2.3400 | 26.7383 | 10.1981 | 22.8642 | 26.0922 | 18.524 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|