File size: 3,642 Bytes
ed3ed3c f04aa61 ed3ed3c f04aa61 ed3ed3c f04aa61 ed3ed3c f04aa61 ed3ed3c f04aa61 ed3ed3c f04aa61 ed3ed3c f04aa61 ed3ed3c |
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 |
---
license: apache-2.0
base_model: Danish-summarisation/DanSumT5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-baseV_38821
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. -->
# DanSumT5-baseV_38821
This model is a fine-tuned version of [Danish-summarisation/DanSumT5-base](https://huggingface.co/Danish-summarisation/DanSumT5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2026
- Rouge1: 34.9358
- Rouge2: 11.6813
- Rougel: 21.4935
- Rougelsum: 27.4979
- Gen Len: 126.3262
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 232 | 2.4684 | 33.3966 | 9.9982 | 19.6472 | 27.3865 | 126.8712 |
| No log | 2.0 | 465 | 2.3905 | 34.2228 | 10.5192 | 20.3584 | 27.4209 | 126.8712 |
| 2.8064 | 3.0 | 697 | 2.3486 | 34.5949 | 11.0682 | 20.8844 | 27.3403 | 126.6738 |
| 2.8064 | 4.0 | 930 | 2.3193 | 34.6865 | 11.0996 | 20.9574 | 27.337 | 126.2318 |
| 2.5767 | 5.0 | 1162 | 2.2963 | 34.3101 | 11.0183 | 20.8461 | 27.155 | 126.721 |
| 2.5767 | 6.0 | 1395 | 2.2774 | 34.9299 | 11.5927 | 21.3549 | 27.7805 | 126.4249 |
| 2.483 | 7.0 | 1627 | 2.2646 | 34.4741 | 11.1383 | 21.2722 | 27.3822 | 126.3004 |
| 2.483 | 8.0 | 1860 | 2.2521 | 34.9384 | 11.2651 | 21.3153 | 27.5792 | 126.9828 |
| 2.4134 | 9.0 | 2092 | 2.2410 | 34.9546 | 11.424 | 21.1427 | 27.6608 | 126.7854 |
| 2.4134 | 10.0 | 2325 | 2.2326 | 34.7566 | 11.5721 | 21.4418 | 27.5167 | 126.7425 |
| 2.3576 | 11.0 | 2557 | 2.2263 | 34.5968 | 11.623 | 21.2384 | 27.365 | 126.4506 |
| 2.3576 | 12.0 | 2790 | 2.2194 | 34.7363 | 11.5612 | 21.47 | 27.6572 | 126.5665 |
| 2.3288 | 13.0 | 3022 | 2.2142 | 34.971 | 11.7203 | 21.49 | 27.7418 | 126.5665 |
| 2.3288 | 14.0 | 3255 | 2.2114 | 34.761 | 11.6621 | 21.3963 | 27.568 | 126.6266 |
| 2.3288 | 15.0 | 3487 | 2.2064 | 34.9197 | 11.5475 | 21.4017 | 27.6388 | 126.3305 |
| 2.2951 | 16.0 | 3720 | 2.2067 | 34.8124 | 11.615 | 21.5177 | 27.605 | 126.3605 |
| 2.2951 | 17.0 | 3952 | 2.2042 | 34.7608 | 11.4738 | 21.3464 | 27.379 | 126.4034 |
| 2.2832 | 18.0 | 4185 | 2.2032 | 34.7593 | 11.6239 | 21.4029 | 27.4669 | 126.2489 |
| 2.2832 | 19.0 | 4417 | 2.2029 | 34.8386 | 11.5919 | 21.4719 | 27.5147 | 126.2318 |
| 2.2571 | 19.96 | 4640 | 2.2026 | 34.9358 | 11.6813 | 21.4935 | 27.4979 | 126.3262 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|