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DanSumT5-smallV_45767V_52355
emilstabil/DanSumT5-smallV_45767V_52355
DanSumT5-smallV_45767V_52355 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
- Dec 4, 2023
- Updated
- Dec 4, 2023
- SHA
- 2ed9665a
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-smallV_45767V_52355
This model is a fine-tuned version of emilstabil/DanSumT5-smallV_45767 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4059
- Rouge1: 34.4784
- Rouge2: 11.7874
- Rougel: 21.2024
- Rougelsum: 32.0698
- Gen Len: 126.0211
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: 3e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 0.99 | 47 | 2.4476 | 34.2545 | 11.5442 | 21.045 | 31.8547 | 126.557 |
| No log | 2.0 | 95 | 2.4372 | 34.4857 | 11.8209 | 21.0909 | 32.063 | 126.5105 |
| No log | 2.99 | 142 | 2.4321 | 33.9859 | 11.5454 | 21.0554 | 31.6295 | 126.5907 |
| No log | 4.0 | 190 | 2.4220 | 34.4331 | 11.7682 | 21.2039 | 32.0393 | 126.827 |
| No log | 4.99 | 237 | 2.4187 | 34.3511 | 11.8482 | 21.4824 | 31.9866 | 126.2447 |
| No log | 6.0 | 285 | 2.4178 | 34.3999 | 12.0167 | 21.3841 | 32.094 | 126.3038 |
| No log | 6.99 | 332 | 2.4119 | 34.0619 | 11.882 | 21.2583 | 31.713 | 126.0338 |
| No log | 8.0 | 380 | 2.4081 | 34.3605 | 11.8248 | 21.3351 | 32.0082 | 125.9409 |
| No log | 8.99 | 427 | 2.4072 | 34.2218 | 11.7791 | 21.3086 | 31.8756 | 125.8186 |
| No log | 10.0 | 475 | 2.4066 | 34.5265 | 11.8846 | 21.3612 | 32.1396 | 125.8608 |
| 2.6208 | 10.88 | 517 | 2.4059 | 34.4784 | 11.7874 | 21.2024 | 32.0698 | 126.0211 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1+git7548e2f
- Datasets 2.13.2
- Tokenizers 0.13.3
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Dec 4, 2023
- Updated
- Dec 4, 2023