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trained-distilbart-abs-0807
roequitz/trained-distilbart-abs-0807
trained-distilbart-abs-0807 is a machine learning model from roequitz. 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
- Jul 8, 2024
- Updated
- Jul 8, 2024
- SHA
- aa2cb5cc
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. -->
trained-distilbart-abs-0807
This model is a fine-tuned version of sshleifer/distilbart-xsum-12-6 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.4184
- Rouge/rouge1: 0.0185
- Rouge/rouge2: 0.0088
- Rouge/rougel: 0.0152
- Rouge/rougelsum: 0.016
- Bertscore/bertscore-precision: 0.0404
- Bertscore/bertscore-recall: 0.04
- Bertscore/bertscore-f1: 0.0402
- Meteor: 0.0163
- Gen Len: 80.0
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.105 | 1.0 | 220 | 2.0884 | 0.4545 | 0.2049 | 0.3881 | 0.3905 | 0.8969 | 0.88 | 0.8882 | 0.3923 | 80.0 |
| 1.8823 | 2.0 | 440 | 2.0066 | 0.3453 | 0.1575 | 0.2965 | 0.2984 | 0.6632 | 0.6547 | 0.6587 | 0.2949 | 80.0 |
| 1.4089 | 3.0 | 660 | 2.0717 | 0.0768 | 0.0337 | 0.0637 | 0.0639 | 0.1559 | 0.1535 | 0.1547 | 0.0667 | 80.0 |
| 1.0687 | 4.0 | 880 | 2.1627 | 0.0125 | 0.0048 | 0.0104 | 0.0114 | 0.0322 | 0.0317 | 0.0319 | 0.0118 | 80.0 |
| 0.7445 | 5.0 | 1100 | 2.2927 | 0.0402 | 0.0177 | 0.0332 | 0.0332 | 0.0815 | 0.0809 | 0.0812 | 0.0374 | 80.0 |
| 0.7619 | 6.0 | 1320 | 2.4184 | 0.0185 | 0.0088 | 0.0152 | 0.016 | 0.0404 | 0.04 | 0.0402 | 0.0163 | 80.0 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Jul 8, 2024
- Updated
- Jul 8, 2024