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bart-large-cnn-finetuned-samsum-v2
amagzari/bart-large-cnn-finetuned-samsum-v2
bart-large-cnn-finetuned-samsum-v2 is a machine learning model from amagzari. 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
- Dec 1, 2022
- Updated
- Dec 1, 2022
- SHA
- 404f5e36
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-large-cnn-finetuned-samsum-v2
This model is a fine-tuned version of facebook/bart-large-cnn on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.3668
- Rouge1: 42.2679
- Rouge2: 21.8061
- Rougel: 32.5227
- Rougelsum: 39.1041
- Gen Len: 59.7738
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
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.3262 | 1.0 | 1841 | 1.3668 | 42.2679 | 21.8061 | 32.5227 | 39.1041 | 59.7738 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Dec 1, 2022
- Updated
- Dec 1, 2022