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mt5-small-task3-dataset2
ZhiguangHan/mt5-small-task3-dataset2
mt5-small-task3-dataset2 is a machine learning model from ZhiguangHan. 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
- Dec 8, 2023
- Updated
- Jan 22, 2024
- SHA
- 012f990e
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-small-task3-dataset2
This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5208
- Accuracy: 0.06
- Mse: 4.0312
- Log-distance: 0.6188
- S Score: 0.5264
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: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Mse | Log-distance | S Score |
|---|---|---|---|---|---|---|---|
| 10.5119 | 1.0 | 250 | 2.2161 | 0.016 | 4.4296 | 0.7765 | 0.4528 |
| 3.0365 | 2.0 | 500 | 1.7090 | 0.026 | 4.5503 | 0.7910 | 0.4512 |
| 2.2209 | 3.0 | 750 | 1.6007 | 0.052 | 4.7537 | 0.6721 | 0.4932 |
| 1.9292 | 4.0 | 1000 | 1.5895 | 0.042 | 4.1466 | 0.6578 | 0.5020 |
| 1.7982 | 5.0 | 1250 | 1.5695 | 0.052 | 4.7583 | 0.6732 | 0.4928 |
| 1.7379 | 6.0 | 1500 | 1.5367 | 0.046 | 4.2149 | 0.6615 | 0.5000 |
| 1.7081 | 7.0 | 1750 | 1.5376 | 0.054 | 4.1174 | 0.6606 | 0.5028 |
| 1.6768 | 8.0 | 2000 | 1.5462 | 0.054 | 4.1031 | 0.6584 | 0.5032 |
| 1.6515 | 9.0 | 2250 | 1.5256 | 0.052 | 4.1256 | 0.6525 | 0.5076 |
| 1.6235 | 10.0 | 2500 | 1.5512 | 0.052 | 4.1063 | 0.6542 | 0.5040 |
| 1.6289 | 11.0 | 2750 | 1.5346 | 0.06 | 4.1069 | 0.6390 | 0.5140 |
| 1.6077 | 12.0 | 3000 | 1.5385 | 0.058 | 4.0832 | 0.6298 | 0.5200 |
| 1.6014 | 13.0 | 3250 | 1.5204 | 0.058 | 3.9666 | 0.6236 | 0.5240 |
| 1.5998 | 14.0 | 3500 | 1.5201 | 0.06 | 4.0911 | 0.6142 | 0.5304 |
| 1.5994 | 15.0 | 3750 | 1.5208 | 0.06 | 4.0312 | 0.6188 | 0.5264 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Dec 8, 2023
- Updated
- Jan 22, 2024