File size: 3,954 Bytes
e36c43a |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnndm_3epoch
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 24.5435
---
<!-- 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-cnndm_3epoch
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6622
- Rouge1: 24.5435
- Rouge2: 11.7919
- Rougel: 20.2929
- Rougelsum: 23.1661
- Gen Len: 18.9996
## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.9113 | 0.14 | 5000 | 1.7162 | 24.4374 | 11.6932 | 20.1741 | 23.0427 | 18.9997 |
| 1.8772 | 0.28 | 10000 | 1.7008 | 24.3715 | 11.6699 | 20.1387 | 22.9772 | 18.9997 |
| 1.8609 | 0.42 | 15000 | 1.6911 | 24.4174 | 11.6986 | 20.1756 | 23.0205 | 18.9997 |
| 1.8564 | 0.56 | 20000 | 1.6871 | 24.4374 | 11.6801 | 20.1663 | 23.0366 | 18.9995 |
| 1.8495 | 0.7 | 25000 | 1.6796 | 24.4019 | 11.6901 | 20.177 | 23.034 | 18.999 |
| 1.8448 | 0.84 | 30000 | 1.6787 | 24.4813 | 11.7227 | 20.1985 | 23.0847 | 18.999 |
| 1.8427 | 0.98 | 35000 | 1.6762 | 24.4905 | 11.7591 | 20.2548 | 23.1006 | 18.9993 |
| 1.8341 | 1.11 | 40000 | 1.6747 | 24.4743 | 11.7124 | 20.1782 | 23.0726 | 18.9996 |
| 1.822 | 1.25 | 45000 | 1.6753 | 24.4797 | 11.7292 | 20.2319 | 23.0816 | 18.9993 |
| 1.8262 | 1.39 | 50000 | 1.6713 | 24.4865 | 11.7079 | 20.2214 | 23.0919 | 18.9986 |
| 1.8281 | 1.53 | 55000 | 1.6702 | 24.5095 | 11.7364 | 20.2534 | 23.1264 | 18.9991 |
| 1.8228 | 1.67 | 60000 | 1.6678 | 24.5153 | 11.7595 | 20.2544 | 23.1138 | 18.9993 |
| 1.824 | 1.81 | 65000 | 1.6662 | 24.5324 | 11.7804 | 20.2671 | 23.1498 | 18.9997 |
| 1.8265 | 1.95 | 70000 | 1.6648 | 24.5795 | 11.7917 | 20.2935 | 23.1855 | 18.9992 |
| 1.8179 | 2.09 | 75000 | 1.6658 | 24.5426 | 11.804 | 20.2861 | 23.1586 | 18.9996 |
| 1.8147 | 2.23 | 80000 | 1.6646 | 24.5429 | 11.7914 | 20.2889 | 23.1542 | 18.9993 |
| 1.8026 | 2.37 | 85000 | 1.6632 | 24.5451 | 11.8045 | 20.2781 | 23.1555 | 18.9996 |
| 1.8141 | 2.51 | 90000 | 1.6643 | 24.5078 | 11.7781 | 20.2631 | 23.121 | 18.9996 |
| 1.8124 | 2.65 | 95000 | 1.6628 | 24.5728 | 11.7958 | 20.2875 | 23.178 | 18.9996 |
| 1.8098 | 2.79 | 100000 | 1.6635 | 24.5534 | 11.7998 | 20.2979 | 23.169 | 18.9996 |
| 1.8153 | 2.93 | 105000 | 1.6622 | 24.5435 | 11.7919 | 20.2929 | 23.1661 | 18.9996 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|