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DanSumT5-baseV_91333
emilstabil/DanSumT5-baseV_91333
DanSumT5-baseV_91333 is a machine learning model from emilstabil. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- mt5
- Access
- Public
- Created
- Nov 18, 2023
- Updated
- Nov 18, 2023
- SHA
- fd1899c3
Base models
Model card
From the Hugging Face model README
<!-- 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-base-finetuned-test_6887
This model is a fine-tuned version of Danish-summarisation/DanSumT5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.5277
- Rouge1: 31.3188
- Rouge2: 7.8236
- Rougel: 17.8296
- Rougelsum: 28.6162
- Gen Len: 127.0
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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 0.99 | 66 | 2.7401 | 29.9221 | 6.3861 | 16.5877 | 27.0611 | 125.09 |
| No log | 1.99 | 133 | 2.6815 | 30.6874 | 6.9413 | 16.9609 | 27.8851 | 125.98 |
| No log | 3.0 | 200 | 2.6440 | 31.2045 | 7.4012 | 17.7421 | 28.3497 | 126.63 |
| No log | 4.0 | 267 | 2.6199 | 31.3329 | 7.4574 | 17.8549 | 28.643 | 126.98 |
| No log | 4.99 | 333 | 2.5984 | 31.5184 | 7.7763 | 17.9153 | 29.0627 | 127.0 |
| No log | 5.99 | 400 | 2.5822 | 31.8839 | 7.9755 | 18.0572 | 29.2282 | 126.65 |
| No log | 7.0 | 467 | 2.5677 | 31.5939 | 7.9515 | 17.865 | 29.2019 | 126.45 |
| 2.8684 | 8.0 | 534 | 2.5587 | 31.4931 | 7.6042 | 17.6853 | 28.8366 | 126.79 |
| 2.8684 | 8.99 | 600 | 2.5496 | 31.105 | 7.6714 | 17.5128 | 28.5242 | 126.78 |
| 2.8684 | 9.99 | 667 | 2.5423 | 31.6087 | 8.0358 | 17.9956 | 28.9514 | 126.78 |
| 2.8684 | 11.0 | 734 | 2.5364 | 31.411 | 7.9534 | 17.895 | 28.7595 | 127.0 |
| 2.8684 | 12.0 | 801 | 2.5326 | 31.4648 | 7.9777 | 17.9589 | 28.8168 | 127.0 |
| 2.8684 | 12.99 | 867 | 2.5296 | 31.374 | 7.8341 | 17.8341 | 28.8146 | 127.0 |
| 2.8684 | 13.99 | 934 | 2.5278 | 31.2822 | 7.7789 | 17.7983 | 28.5903 | 127.0 |
| 2.8684 | 14.83 | 990 | 2.5277 | 31.3188 | 7.8236 | 17.8296 | 28.6162 | 127.0 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Nov 18, 2023
- Updated
- Nov 18, 2023