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distilbart-summarization-top-single-fulllayers
VexPoli/distilbart-summarization-top-single-fulllayers
distilbart-summarization-top-single-fulllayers is a machine learning model from VexPoli. 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
- bart
- Access
- Public
- Created
- Feb 12, 2025
- Updated
- Feb 12, 2025
- SHA
- 144f2fd3
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. -->
distilbart-summarization-top-single-fulllayers
This model is a fine-tuned version of sshleifer/distilbart-xsum-6-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8382
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.1269 | 0.1882 | 500 | 2.0373 |
| 2.0121 | 0.3764 | 1000 | 1.9665 |
| 1.9967 | 0.5645 | 1500 | 1.9280 |
| 1.9512 | 0.7527 | 2000 | 1.8957 |
| 1.9301 | 0.9409 | 2500 | 1.8763 |
| 1.7417 | 1.1291 | 3000 | 1.8685 |
| 1.7184 | 1.3173 | 3500 | 1.8584 |
| 1.7206 | 1.5055 | 4000 | 1.8503 |
| 1.6814 | 1.6936 | 4500 | 1.8437 |
| 1.756 | 1.8818 | 5000 | 1.8382 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Feb 12, 2025
- Updated
- Feb 12, 2025