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tidy-tab-model-bart-large-cnn
wgcv/tidy-tab-model-bart-large-cnn
tidy-tab-model-bart-large-cnn is a machine learning model from wgcv. 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 mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- bart
- Access
- Public
- Created
- Jul 9, 2024
- Updated
- Jul 9, 2024
- SHA
- 9766a0a7
Base models
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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. -->
tidy-tab-model-bart-large-cnn
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.5251
- Rouge1: 0.1756
- Rouge2: 0.106
- Rougel: 0.1736
- Rougelsum: 0.1739
- Gen Len: 57.7607
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: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 24
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.0787 | 7.5758 | 500 | 2.4807 | 0.1848 | 0.1066 | 0.1798 | 0.18 | 57.5385 |
| 0.0293 | 15.1515 | 1000 | 2.1823 | 0.1619 | 0.0957 | 0.1594 | 0.1594 | 57.7521 |
| 0.0072 | 22.7273 | 1500 | 2.5251 | 0.1756 | 0.106 | 0.1736 | 0.1739 | 57.7607 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Jul 9, 2024
- Updated
- Jul 9, 2024