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text_shortening_model_v47
ldos/text_shortening_model_v47
text_shortening_model_v47 is a machine learning model from ldos. 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 21, 2023
- Updated
- Sep 21, 2023
- SHA
- 93beb1e8
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. -->
text_shortening_model_v47
This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.3912
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Bert precision: 0.6047
- Bert recall: 0.5681
- Average word count: 1.0
- Max word count: 1
- Min word count: 1
- Average token count: 12.0
- % shortened texts with length > 12: 0.0
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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7.822 | 1.0 | 83 | 7.4737 | 0.0776 | 0.0 | 0.0775 | 0.0776 | 0.6348 | 0.6223 | 2.0 | 2 | 2 | 13.0 | 0.0 |
| 3.2859 | 2.0 | 166 | 6.6585 | 0.1063 | 0.0 | 0.1063 | 0.1063 | 0.6469 | 0.608 | 5.0026 | 6 | 5 | 12.0 | 0.0 |
| 3.0284 | 3.0 | 249 | 6.4761 | 0.116 | 0.0 | 0.116 | 0.1161 | 0.6479 | 0.6388 | 3.9974 | 4 | 3 | 14.0 | 0.0 |
| 2.9681 | 4.0 | 332 | 6.4592 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6071 | 0.5723 | 1.0 | 1 | 1 | 12.0 | 0.0 |
| 2.9377 | 5.0 | 415 | 6.4142 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.5681 | 1.0 | 1 | 1 | 12.0 | 0.0 |
| 2.9168 | 6.0 | 498 | 6.4049 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6049 | 0.5685 | 1.0 | 1 | 1 | 12.0 | 0.0 |
| 2.8964 | 7.0 | 581 | 6.3912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6047 | 0.5681 | 1.0 | 1 | 1 | 12.0 | 0.0 |
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 21, 2023
- Updated
- Sep 21, 2023