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bart-abs-2409-1947-lr-3e-05-bs-8-maxep-6
roequitz/bart-abs-2409-1947-lr-3e-05-bs-8-maxep-6
bart-abs-2409-1947-lr-3e-05-bs-8-maxep-6 is a machine learning model from roequitz. 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 apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- bart
- Access
- Public
- Created
- Sep 24, 2024
- Updated
- Sep 24, 2024
- SHA
- c25ac4a6
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-abs-2409-1947-lr-3e-05-bs-8-maxep-6
This model is a fine-tuned version of sshleifer/distilbart-xsum-12-6 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.8980
- Rouge/rouge1: 0.462
- Rouge/rouge2: 0.2198
- Rouge/rougel: 0.4014
- Rouge/rougelsum: 0.4029
- Bertscore/bertscore-precision: 0.8957
- Bertscore/bertscore-recall: 0.8926
- Bertscore/bertscore-f1: 0.894
- Meteor: 0.4124
- Gen Len: 37.3727
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: 3e-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
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1721 | 1.0 | 109 | 3.5761 | 0.4573 | 0.2036 | 0.3842 | 0.3851 | 0.8952 | 0.8917 | 0.8933 | 0.405 | 36.9727 |
| 0.1505 | 2.0 | 218 | 3.5724 | 0.45 | 0.1979 | 0.3854 | 0.3855 | 0.8956 | 0.8912 | 0.8933 | 0.3964 | 36.3273 |
| 0.1132 | 3.0 | 327 | 3.7251 | 0.4535 | 0.2052 | 0.385 | 0.3867 | 0.8955 | 0.8917 | 0.8934 | 0.4043 | 37.4273 |
| 0.0881 | 4.0 | 436 | 3.8461 | 0.4676 | 0.2097 | 0.397 | 0.3983 | 0.8967 | 0.8917 | 0.8941 | 0.4047 | 35.7091 |
| 0.0734 | 5.0 | 545 | 3.8385 | 0.458 | 0.21 | 0.3905 | 0.3919 | 0.8939 | 0.8927 | 0.8931 | 0.4105 | 38.0182 |
| 0.0612 | 6.0 | 654 | 3.8980 | 0.462 | 0.2198 | 0.4014 | 0.4029 | 0.8957 | 0.8926 | 0.894 | 0.4124 | 37.3727 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Sep 24, 2024
- Updated
- Sep 24, 2024