metadata
license: apache-2.0
base_model: Danish-summarisation/DanSumT5-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-largeV_84227
results: []
DanSumT5-largeV_84227
This model is a fine-tuned version of Danish-summarisation/DanSumT5-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2976
- Rouge1: 32.3488
- Rouge2: 8.638
- Rougel: 18.8215
- Rougelsum: 29.8654
- Gen Len: 126.28
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 | 200 | 2.5620 | 31.6386 | 7.3603 | 17.9932 | 28.8935 | 126.32 |
| No log | 2.0 | 400 | 2.4824 | 31.8478 | 8.0477 | 18.5952 | 29.2582 | 126.77 |
| 2.7655 | 3.0 | 600 | 2.4305 | 32.1965 | 8.4935 | 18.7317 | 29.9719 | 125.03 |
| 2.7655 | 4.0 | 800 | 2.3945 | 31.8539 | 8.7262 | 18.5421 | 29.8472 | 125.63 |
| 2.4368 | 5.0 | 1000 | 2.3685 | 32.0137 | 8.2933 | 18.7818 | 29.561 | 125.32 |
| 2.4368 | 6.0 | 1200 | 2.3522 | 31.5 | 8.3477 | 18.9478 | 29.3072 | 125.11 |
| 2.4368 | 7.0 | 1400 | 2.3364 | 31.6482 | 8.3012 | 18.9953 | 29.0985 | 123.38 |
| 2.2645 | 8.0 | 1600 | 2.3250 | 31.9939 | 8.5944 | 18.9914 | 29.5092 | 125.18 |
| 2.2645 | 9.0 | 1800 | 2.3212 | 31.5611 | 8.1969 | 18.7941 | 29.151 | 126.01 |
| 2.134 | 10.0 | 2000 | 2.3117 | 32.0902 | 8.6962 | 19.0793 | 29.758 | 125.4 |
| 2.134 | 11.0 | 2200 | 2.3064 | 31.9365 | 8.7161 | 18.9113 | 29.6812 | 125.86 |
| 2.134 | 12.0 | 2400 | 2.3062 | 32.3185 | 9.0913 | 19.2692 | 29.9962 | 126.24 |
| 2.0467 | 13.0 | 2600 | 2.3032 | 31.7591 | 8.4993 | 18.8326 | 29.4231 | 125.02 |
| 2.0467 | 14.0 | 2800 | 2.3008 | 32.0532 | 8.8654 | 18.897 | 29.5819 | 126.2 |
| 1.9931 | 15.0 | 3000 | 2.2980 | 31.8987 | 8.7669 | 19.0859 | 29.3799 | 126.0 |
| 1.9931 | 16.0 | 3200 | 2.2982 | 32.2458 | 8.7896 | 18.6845 | 29.6991 | 126.0 |
| 1.9931 | 17.0 | 3400 | 2.2987 | 32.0869 | 8.6678 | 18.7656 | 29.8441 | 125.66 |
| 1.949 | 18.0 | 3600 | 2.2974 | 32.1759 | 8.6004 | 18.7892 | 29.6918 | 126.31 |
| 1.949 | 19.0 | 3800 | 2.2970 | 32.1139 | 8.5827 | 18.7099 | 29.5327 | 126.15 |
| 1.9257 | 20.0 | 4000 | 2.2976 | 32.3488 | 8.638 | 18.8215 | 29.8654 | 126.28 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3