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pegasus-newsroom-summarizer_02
chinhon/pegasus-newsroom-summarizer_02
pegasus-newsroom-summarizer_02 is a machine learning model from chinhon. 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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- pegasus
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jan 18, 2024
- SHA
- 4a4ec274
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. -->
pegasus-newsroom-summarizer_02
This model is a fine-tuned version of google/pegasus-newsroom on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2204
- Rouge1: 52.4459
- Rouge2: 35.2568
- Rougel: 41.6213
- Rougelsum: 48.7859
- Gen Len: 98.0627
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.3231 | 1.0 | 16113 | 1.2305 | 52.1565 | 34.8681 | 41.3189 | 48.4258 | 95.9049 |
| 1.3001 | 2.0 | 32226 | 1.2186 | 52.4921 | 35.2661 | 41.6264 | 48.8168 | 98.9241 |
| 1.2372 | 3.0 | 48339 | 1.2204 | 52.4459 | 35.2568 | 41.6213 | 48.7859 | 98.0627 |
Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
- Library
- transformers
- Type
- pegasus
- Created
- Mar 2, 2022
- Updated
- Jan 18, 2024