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mt5-base_V25775
emilstabil/mt5-base_V25775
mt5-base_V25775 is a machine learning model from emilstabil. 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
- mt5
- Access
- Public
- Created
- Nov 18, 2023
- Updated
- Nov 19, 2023
- SHA
- e215a9af
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. -->
mt5-base_V25775
This model is a fine-tuned version of google/mt5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1251
- Rouge1: 28.2174
- Rouge2: 10.5032
- Rougel: 19.8511
- Rougelsum: 23.3756
- Gen Len: 72.3391
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: 3
- eval_batch_size: 3
- 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 |
|---|---|---|---|---|---|---|---|---|
| 6.8396 | 0.81 | 500 | 2.5158 | 15.7446 | 6.4867 | 12.6043 | 13.8967 | 31.1588 |
| 3.1783 | 1.61 | 1000 | 2.3673 | 21.0031 | 8.2119 | 15.9097 | 17.9958 | 46.0086 |
| 3.0094 | 2.42 | 1500 | 2.3091 | 20.5903 | 8.1394 | 15.7354 | 17.696 | 44.7339 |
| 2.8754 | 3.23 | 2000 | 2.2652 | 22.3129 | 8.6681 | 16.4687 | 18.8755 | 48.9485 |
| 2.7643 | 4.03 | 2500 | 2.2320 | 22.6675 | 8.8846 | 16.7258 | 19.0948 | 48.6781 |
| 2.7 | 4.84 | 3000 | 2.2190 | 24.1409 | 9.4362 | 17.7197 | 20.2512 | 52.8498 |
| 2.6373 | 5.65 | 3500 | 2.2100 | 24.594 | 9.4296 | 18.0182 | 20.6398 | 55.0687 |
| 2.6182 | 6.45 | 4000 | 2.2016 | 25.0763 | 9.432 | 18.1113 | 20.6752 | 57.4549 |
| 2.5552 | 7.26 | 4500 | 2.1767 | 26.6143 | 10.1357 | 19.004 | 22.0372 | 62.6738 |
| 2.5319 | 8.06 | 5000 | 2.1665 | 27.0349 | 10.3809 | 19.3472 | 22.5876 | 64.7167 |
| 2.5145 | 8.87 | 5500 | 2.1705 | 26.6323 | 9.956 | 18.9994 | 22.119 | 62.3176 |
| 2.4923 | 9.68 | 6000 | 2.1499 | 27.0052 | 10.0351 | 19.2887 | 22.4559 | 64.2747 |
| 2.4367 | 10.48 | 6500 | 2.1418 | 27.0134 | 10.1253 | 19.2614 | 22.4648 | 65.2661 |
| 2.4312 | 11.29 | 7000 | 2.1503 | 27.1655 | 9.9501 | 19.1768 | 22.3967 | 66.6953 |
| 2.4186 | 12.1 | 7500 | 2.1370 | 26.6422 | 9.7971 | 19.0065 | 22.0444 | 65.9571 |
| 2.3977 | 12.9 | 8000 | 2.1395 | 27.5204 | 10.3095 | 19.4189 | 22.7497 | 69.0901 |
| 2.3596 | 13.71 | 8500 | 2.1302 | 27.685 | 10.1479 | 19.4521 | 22.7892 | 70.0644 |
| 2.3951 | 14.52 | 9000 | 2.1298 | 27.8389 | 10.2493 | 19.6671 | 22.933 | 70.7897 |
| 2.3433 | 15.32 | 9500 | 2.1238 | 27.9095 | 10.33 | 19.6428 | 22.9721 | 70.4206 |
| 2.3789 | 16.13 | 10000 | 2.1271 | 28.0755 | 10.5819 | 19.9535 | 23.2605 | 69.97 |
| 2.3331 | 16.94 | 10500 | 2.1240 | 28.1362 | 10.4656 | 19.8198 | 23.1857 | 70.9485 |
| 2.3395 | 17.74 | 11000 | 2.1245 | 28.1459 | 10.4803 | 19.801 | 23.2469 | 71.1288 |
| 2.3238 | 18.55 | 11500 | 2.1273 | 28.2156 | 10.4437 | 19.858 | 23.3457 | 73.485 |
| 2.3181 | 19.35 | 12000 | 2.1251 | 28.2174 | 10.5032 | 19.8511 | 23.3756 | 72.3391 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Nov 18, 2023
- Updated
- Nov 19, 2023