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bart-base-finetuned-xsum
lacos03/bart-base-finetuned-xsum
bart-base-finetuned-xsum is a machine learning model from lacos03. 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
- Jun 4, 2025
- Updated
- Jun 20, 2025
- SHA
- a92ca2d3
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-base-finetuned-xsum
This model is a fine-tuned version of facebook/bart-base on the xsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.7558
- Rouge1: 38.6459
- Rouge2: 17.3528
- Rougel: 31.9807
- Rougelsum: 31.9765
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: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 12753 | 1.8305 | 37.5583 | 16.2117 | 30.8468 | 30.842 |
| No log | 2.0 | 25506 | 1.7558 | 38.6459 | 17.3528 | 31.9807 | 31.9765 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Jun 4, 2025
- Updated
- Jun 20, 2025