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DanSumT5-smallV_91332
emilstabil/DanSumT5-smallV_91332
DanSumT5-smallV_91332 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 24, 2023
- Updated
- Nov 28, 2023
- SHA
- f1df7c42
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_91332
This model is a fine-tuned version of Danish-summarisation/DanSumT5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.6864
- Rouge1: 31.4187
- Rouge2: 8.4053
- Rougel: 18.0276
- Rougelsum: 28.7915
- Gen Len: 126.8529
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 0.97 | 29 | 2.7126 | 31.1174 | 7.9711 | 17.6697 | 28.3148 | 126.521 |
| No log | 1.95 | 58 | 2.6864 | 31.4187 | 8.4053 | 18.0276 | 28.7915 | 126.8529 |
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
- Nov 24, 2023
- Updated
- Nov 28, 2023