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bart-cnn-samsum-peft
SiyuK/bart-cnn-samsum-peft
bart-cnn-samsum-peft is a machine learning model from SiyuK. 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 peft. 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
- peft
- License
- mit
- Access
- Public
- Created
- Jun 5, 2024
- Updated
- Jun 6, 2024
- SHA
- fce2a4b8
Base models
Datasets
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. -->
bart-cnn-samsum-peft
This model is a fine-tuned version of facebook/bart-large-cnn on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 0.4334
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 |
|---|---|---|---|
| 0.4513 | 1.0 | 37 | 0.5301 |
| 0.45 | 2.0 | 74 | 0.5104 |
| 0.4505 | 3.0 | 111 | 0.4969 |
| 0.4882 | 4.0 | 148 | 0.4857 |
| 0.4246 | 5.0 | 185 | 0.4759 |
| 0.386 | 6.0 | 222 | 0.4672 |
| 0.3959 | 7.0 | 259 | 0.4599 |
| 0.4179 | 8.0 | 296 | 0.4536 |
| 0.427 | 9.0 | 333 | 0.4480 |
| 0.3997 | 10.0 | 370 | 0.4435 |
| 0.4041 | 11.0 | 407 | 0.4400 |
| 0.4021 | 12.0 | 444 | 0.4371 |
| 0.3551 | 13.0 | 481 | 0.4351 |
| 0.3619 | 14.0 | 518 | 0.4338 |
| 0.3768 | 15.0 | 555 | 0.4334 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Library
- peft
- License
- mit
- Created
- Jun 5, 2024
- Updated
- Jun 6, 2024