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t5-large-slots
jinlmsft/t5-large-slots
t5-large-slots is a machine learning model from jinlmsft. 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
- t5
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Feb 8, 2022
- SHA
- fa6c747d
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. -->
t5-large-slots
This model is a fine-tuned version of t5-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0889
- Acc: 0.76
- True Num: 11167
- Num: 14748
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc | True Num | Num |
|---|---|---|---|---|---|---|
| 0.3539 | 0.56 | 1000 | 0.2669 | 0.56 | 8264 | 14748 |
| 0.2523 | 1.13 | 2000 | 0.2031 | 0.56 | 8317 | 14748 |
| 0.2003 | 1.69 | 3000 | 0.1498 | 0.58 | 8496 | 14748 |
| 0.1609 | 2.25 | 4000 | 0.1284 | 0.58 | 8612 | 14748 |
| 0.1431 | 2.82 | 5000 | 0.1119 | 0.59 | 8675 | 14748 |
| 0.1236 | 3.38 | 6000 | 0.1054 | 0.59 | 8737 | 14748 |
| 0.1172 | 3.95 | 7000 | 0.0981 | 0.59 | 8773 | 14748 |
| 0.1027 | 4.51 | 8000 | 0.0955 | 0.6 | 8787 | 14748 |
| 0.0968 | 5.07 | 9000 | 0.0931 | 0.6 | 8807 | 14748 |
| 0.0911 | 5.64 | 10000 | 0.0895 | 0.6 | 8787 | 14748 |
| 0.0852 | 6.2 | 11000 | 0.0912 | 0.6 | 8840 | 14748 |
| 0.0823 | 6.76 | 12000 | 0.0880 | 0.6 | 8846 | 14748 |
| 0.0768 | 7.33 | 13000 | 0.0915 | 0.6 | 8879 | 14748 |
| 0.0758 | 7.89 | 14000 | 0.0892 | 0.6 | 8853 | 14748 |
| 0.0708 | 8.46 | 15000 | 0.0885 | 0.6 | 8884 | 14748 |
| 0.0701 | 9.02 | 16000 | 0.0884 | 0.6 | 8915 | 14748 |
| 0.0685 | 9.58 | 17000 | 0.0884 | 0.6 | 8921 | 14748 |
Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Feb 8, 2022