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bart-large-finetuned-billsum
luluw/bart-large-finetuned-billsum
bart-large-finetuned-billsum is a machine learning model from luluw. 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
- Aug 27, 2024
- Updated
- Sep 6, 2024
- SHA
- 1d8292e4
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. -->
bart-large-finetuned-billsum
This model is a fine-tuned version of facebook/bart-large-xsum on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1947
- Rouge1: 35.1575
- Rouge2: 27.7021
- Rougel: 32.9801
- Rougelsum: 33.6194
- Gen Len: 31.9873
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.5279 | 0.4221 | 1000 | 1.3638 | 34.2853 | 26.1627 | 31.897 | 32.6399 | 31.999 |
| 1.3237 | 0.8442 | 2000 | 1.2357 | 34.7055 | 26.7936 | 32.3811 | 33.0823 | 31.9973 |
| 1.1594 | 1.2664 | 3000 | 1.2246 | 34.6975 | 27.0964 | 32.5326 | 33.1883 | 31.982 |
| 1.1029 | 1.6885 | 4000 | 1.2092 | 34.4969 | 26.9107 | 32.3644 | 33.0481 | 31.9987 |
| 1.0461 | 2.1106 | 5000 | 1.1769 | 35.2419 | 27.6038 | 33.0339 | 33.6849 | 31.9903 |
| 0.9535 | 2.5327 | 6000 | 1.1958 | 34.7138 | 27.2185 | 32.5573 | 33.2043 | 31.9947 |
| 0.9373 | 2.9548 | 7000 | 1.1600 | 35.1741 | 27.6199 | 32.9618 | 33.6181 | 31.9783 |
| 0.8506 | 3.3770 | 8000 | 1.1940 | 34.8976 | 27.4455 | 32.7581 | 33.4013 | 31.99 |
| 0.8341 | 3.7991 | 9000 | 1.1716 | 35.1191 | 27.6856 | 32.9822 | 33.6221 | 31.9853 |
| 0.8083 | 4.2212 | 10000 | 1.1916 | 35.1839 | 27.7013 | 32.995 | 33.6131 | 31.988 |
| 0.7749 | 4.6433 | 11000 | 1.1947 | 35.1575 | 27.7021 | 32.9801 | 33.6194 | 31.9873 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.2.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Aug 27, 2024
- Updated
- Sep 6, 2024