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sum_model_2r1e_3_20_extract_long_text
weny22/sum_model_2r1e_3_20_extract_long_text
sum_model_2r1e_3_20_extract_long_text is a machine learning model from weny22. 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
- t5
- Access
- Public
- Created
- Mar 26, 2024
- Updated
- Mar 26, 2024
- SHA
- 4c0721d2
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. -->
sum_model_2r1e_3_20_extract_long_text
only extract text > 500 word
number of sentence in train data = 12 number of sentence in validation data = 12 words in train data: 350 words in summury: 32 after modification: 294 words in validation data: 344 words in summury: 32 after modification 294
This model is a fine-tuned version of weny22/sum_model_t5_saved on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0769
- Rouge1: 0.2174
- Rouge2: 0.0883
- Rougel: 0.1783
- Rougelsum: 0.1783
- Gen Len: 18.9707
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: 0.002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 335 | 2.1718 | 0.1974 | 0.0646 | 0.1569 | 0.1572 | 18.9793 |
| 2.7704 | 2.0 | 670 | 2.0191 | 0.2026 | 0.0737 | 0.1627 | 0.1629 | 18.99 |
| 2.1753 | 3.0 | 1005 | 1.9912 | 0.209 | 0.0792 | 0.1703 | 0.1704 | 18.9607 |
| 2.1753 | 4.0 | 1340 | 1.9403 | 0.2051 | 0.0797 | 0.1673 | 0.1673 | 18.9793 |
| 1.9544 | 5.0 | 1675 | 1.9172 | 0.2162 | 0.0857 | 0.1766 | 0.1766 | 18.9853 |
| 1.8287 | 6.0 | 2010 | 1.9292 | 0.2115 | 0.0829 | 0.1719 | 0.172 | 18.9727 |
| 1.8287 | 7.0 | 2345 | 1.9303 | 0.2113 | 0.0838 | 0.1722 | 0.1721 | 18.972 |
| 1.6903 | 8.0 | 2680 | 1.9165 | 0.2146 | 0.0843 | 0.1754 | 0.1756 | 18.972 |
| 1.6063 | 9.0 | 3015 | 1.9242 | 0.2166 | 0.0864 | 0.1764 | 0.1765 | 18.9707 |
| 1.6063 | 10.0 | 3350 | 1.9196 | 0.2143 | 0.0843 | 0.175 | 0.1749 | 18.986 |
| 1.4965 | 11.0 | 3685 | 1.9313 | 0.2148 | 0.0847 | 0.1743 | 0.1742 | 18.9893 |
| 1.4333 | 12.0 | 4020 | 1.9512 | 0.2165 | 0.0873 | 0.1777 | 0.1778 | 18.99 |
| 1.4333 | 13.0 | 4355 | 1.9756 | 0.2163 | 0.0882 | 0.1779 | 0.178 | 18.9747 |
| 1.346 | 14.0 | 4690 | 1.9945 | 0.2182 | 0.0893 | 0.1792 | 0.1792 | 18.9873 |
| 1.2887 | 15.0 | 5025 | 2.0073 | 0.2154 | 0.085 | 0.1756 | 0.1758 | 18.9873 |
| 1.2887 | 16.0 | 5360 | 2.0176 | 0.2166 | 0.0878 | 0.1772 | 0.1773 | 18.9687 |
| 1.2234 | 17.0 | 5695 | 2.0302 | 0.2172 | 0.0873 | 0.178 | 0.178 | 18.9767 |
| 1.1833 | 18.0 | 6030 | 2.0505 | 0.2156 | 0.0854 | 0.1761 | 0.1761 | 18.9653 |
| 1.1833 | 19.0 | 6365 | 2.0633 | 0.2174 | 0.0875 | 0.1777 | 0.1778 | 18.9787 |
| 1.1396 | 20.0 | 6700 | 2.0769 | 0.2174 | 0.0883 | 0.1783 | 0.1783 | 18.9707 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Library
- transformers
- Type
- t5
- Created
- Mar 26, 2024
- Updated
- Mar 26, 2024