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bart-samsum
Venkatesh4342/bart-samsum
bart-samsum is a machine learning model from Venkatesh4342. 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
- Sep 8, 2023
- Updated
- Sep 8, 2023
- SHA
- 211babaf
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-samsum
This model is a fine-tuned version of facebook/bart-large-xsum on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.3852
- Rouge1: 0.547
- Rouge2: 0.2837
- Rougel: 0.4462
- Rougelsum: 0.4454
- Gen Len: 29.72
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.5201 | 0.27 | 500 | 1.4589 | 0.5276 | 0.2694 | 0.4246 | 0.424 | 33.5067 |
| 1.3757 | 0.54 | 1000 | 1.5105 | 0.506 | 0.2566 | 0.415 | 0.4146 | 29.76 |
| 1.3496 | 0.81 | 1500 | 1.4039 | 0.5365 | 0.2759 | 0.4233 | 0.4221 | 29.8 |
| 1.094 | 1.09 | 2000 | 1.4119 | 0.5407 | 0.2827 | 0.4293 | 0.4288 | 29.84 |
| 1.1488 | 1.36 | 2500 | 1.3680 | 0.5275 | 0.2637 | 0.423 | 0.4224 | 26.92 |
| 1.1222 | 1.63 | 3000 | 1.2875 | 0.5369 | 0.2844 | 0.4473 | 0.4463 | 29.2267 |
| 1.1092 | 1.9 | 3500 | 1.3968 | 0.533 | 0.2818 | 0.4354 | 0.4363 | 30.0667 |
| 0.8509 | 2.17 | 4000 | 1.3682 | 0.5306 | 0.2874 | 0.4327 | 0.4331 | 29.1467 |
| 0.9565 | 2.44 | 4500 | 1.3450 | 0.5466 | 0.2782 | 0.4419 | 0.4409 | 29.2133 |
| 0.8496 | 2.72 | 5000 | 1.3768 | 0.5366 | 0.2807 | 0.4359 | 0.4351 | 30.7733 |
| 0.8397 | 2.99 | 5500 | 1.3852 | 0.547 | 0.2837 | 0.4462 | 0.4454 | 29.72 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Sep 8, 2023
- Updated
- Sep 8, 2023