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--- |
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license: apache-2.0 |
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base_model: Danish-summarisation/DanSumT5-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: DanSumT5-largeV_26719 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# DanSumT5-largeV_26719 |
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This model is a fine-tuned version of [Danish-summarisation/DanSumT5-large](https://huggingface.co/Danish-summarisation/DanSumT5-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2976 |
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- Rouge1: 32.2799 |
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- Rouge2: 8.6728 |
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- Rougel: 18.8723 |
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- Rougelsum: 29.7852 |
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- Gen Len: 126.28 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
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| No log | 1.0 | 200 | 2.5620 | 31.648 | 7.4069 | 17.9711 | 28.7951 | 126.32 | |
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| No log | 2.0 | 400 | 2.4824 | 31.8545 | 8.094 | 18.6072 | 29.1646 | 126.77 | |
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| 2.7655 | 3.0 | 600 | 2.4305 | 32.1209 | 8.5372 | 18.744 | 29.8788 | 125.03 | |
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| 2.7655 | 4.0 | 800 | 2.3945 | 31.8225 | 8.739 | 18.5656 | 29.7696 | 125.63 | |
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| 2.4368 | 5.0 | 1000 | 2.3685 | 31.9779 | 8.322 | 18.766 | 29.4834 | 125.32 | |
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| 2.4368 | 6.0 | 1200 | 2.3522 | 31.4296 | 8.3578 | 18.9591 | 29.2204 | 125.11 | |
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| 2.4368 | 7.0 | 1400 | 2.3364 | 31.5372 | 8.2997 | 18.9915 | 29.0248 | 123.38 | |
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| 2.2645 | 8.0 | 1600 | 2.3250 | 31.9344 | 8.596 | 19.0022 | 29.4647 | 125.18 | |
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| 2.2645 | 9.0 | 1800 | 2.3212 | 31.515 | 8.2166 | 18.7697 | 29.06 | 126.01 | |
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| 2.134 | 10.0 | 2000 | 2.3117 | 32.0188 | 8.6934 | 19.1051 | 29.6682 | 125.4 | |
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| 2.134 | 11.0 | 2200 | 2.3064 | 31.8417 | 8.7247 | 18.9249 | 29.5626 | 125.86 | |
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| 2.134 | 12.0 | 2400 | 2.3062 | 32.2302 | 9.1081 | 19.3087 | 29.9162 | 126.24 | |
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| 2.0467 | 13.0 | 2600 | 2.3032 | 31.6755 | 8.5093 | 18.8486 | 29.365 | 125.02 | |
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| 2.0467 | 14.0 | 2800 | 2.3008 | 31.9478 | 8.8669 | 18.9299 | 29.504 | 126.2 | |
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| 1.9931 | 15.0 | 3000 | 2.2980 | 31.8088 | 8.7506 | 19.1051 | 29.2949 | 126.0 | |
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| 1.9931 | 16.0 | 3200 | 2.2982 | 32.175 | 8.8114 | 18.7002 | 29.6088 | 126.0 | |
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| 1.9931 | 17.0 | 3400 | 2.2987 | 32.0016 | 8.7223 | 18.7814 | 29.6822 | 125.66 | |
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| 1.949 | 18.0 | 3600 | 2.2974 | 32.0515 | 8.6141 | 18.7833 | 29.6024 | 126.31 | |
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| 1.949 | 19.0 | 3800 | 2.2970 | 32.0716 | 8.6257 | 18.7301 | 29.4506 | 126.15 | |
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| 1.9257 | 20.0 | 4000 | 2.2976 | 32.2799 | 8.6728 | 18.8723 | 29.7852 | 126.28 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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