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rut5-base-summ-dialogsum
Kekega/rut5-base-summ-dialogsum
rut5-base-summ-dialogsum is a machine learning model from Kekega. 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
- Dec 20, 2023
- Updated
- Dec 20, 2023
- SHA
- 40f1de3e
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. -->
rut5-base-summ-dialogsum
This model is a fine-tuned version of d0rj/rut5-base-summ on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1263
- Rouge1: 33.5111
- Rouge2: 0.1696
- Rougel: 33.4559
- Rougelsum: 33.4934
- Gen Len: 4.1546
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: 5e-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: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.0946 | 1.0 | 786 | 1.7462 | 45.4252 | 0.0 | 45.4009 | 45.4139 | 4.0464 |
| 1.7182 | 2.0 | 1572 | 1.5005 | 44.9295 | 0.0 | 44.9183 | 44.9108 | 4.1126 |
| 1.5304 | 3.0 | 2358 | 1.3826 | 39.5888 | 0.0 | 39.5811 | 39.5646 | 4.1698 |
| 1.4261 | 4.0 | 3144 | 1.3121 | 30.1735 | 0.0 | 30.1127 | 30.1415 | 4.1520 |
| 1.3252 | 5.0 | 3930 | 1.2641 | 35.7738 | 0.0 | 35.7408 | 35.7858 | 3.8791 |
| 1.2878 | 6.0 | 4716 | 1.2353 | 33.0773 | 0.0 | 32.9682 | 33.0551 | 3.7252 |
| 1.2068 | 7.0 | 5502 | 1.2051 | 34.4094 | 0.0 | 34.3902 | 34.3884 | 3.7729 |
| 1.1763 | 8.0 | 6288 | 1.1952 | 33.0914 | 0.1908 | 33.0267 | 33.0472 | 3.9739 |
| 1.1346 | 9.0 | 7074 | 1.1798 | 33.9606 | 0.0 | 33.9335 | 33.979 | 4.1768 |
| 1.1044 | 10.0 | 7860 | 1.1632 | 32.9529 | 0.0 | 32.9367 | 32.9396 | 4.1673 |
| 1.1073 | 11.0 | 8646 | 1.1499 | 34.0904 | 0.0 | 34.0659 | 34.1317 | 4.1934 |
| 1.0619 | 12.0 | 9432 | 1.1516 | 32.9502 | 0.0 | 32.9056 | 32.9376 | 4.0312 |
| 1.0365 | 13.0 | 10218 | 1.1478 | 31.68 | 0.0 | 31.6488 | 31.7003 | 4.0293 |
| 1.0161 | 14.0 | 11004 | 1.1427 | 32.6651 | 0.0424 | 32.6345 | 32.6538 | 4.1113 |
| 0.9805 | 15.0 | 11790 | 1.1343 | 34.0304 | 0.0636 | 33.9433 | 33.999 | 4.0674 |
| 0.9661 | 16.0 | 12576 | 1.1309 | 34.8704 | 0.0848 | 34.8014 | 34.8501 | 4.0681 |
| 0.9511 | 17.0 | 13362 | 1.1348 | 32.8744 | 0.0 | 32.8277 | 32.8547 | 4.1081 |
| 0.9392 | 18.0 | 14148 | 1.1326 | 32.9349 | 0.1908 | 32.8895 | 32.9376 | 4.2627 |
| 0.9341 | 19.0 | 14934 | 1.1263 | 33.5111 | 0.1696 | 33.4559 | 33.4934 | 4.1546 |
| 0.9396 | 20.0 | 15720 | 1.1349 | 33.9121 | 0.2545 | 33.8438 | 33.8993 | 4.1705 |
| 0.9314 | 21.0 | 16506 | 1.1276 | 33.0779 | 0.106 | 33.0546 | 33.0903 | 4.1399 |
| 0.8987 | 22.0 | 17292 | 1.1333 | 33.8566 | 0.1696 | 33.7943 | 33.843 | 4.1419 |
| 0.8895 | 23.0 | 18078 | 1.1343 | 33.6108 | 0.1484 | 33.5738 | 33.636 | 4.2328 |
| 0.8847 | 24.0 | 18864 | 1.1355 | 33.4257 | 0.2757 | 33.3804 | 33.4495 | 4.1711 |
| 0.8832 | 25.0 | 19650 | 1.1355 | 33.6211 | 0.3393 | 33.5937 | 33.636 | 4.1959 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
- Library
- transformers
- Type
- t5
- Created
- Dec 20, 2023
- Updated
- Dec 20, 2023