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flan-t5-large-extraction-all-dm_4000-ep10-nonstop
Zekunli/flan-t5-large-extraction-all-dm_4000-ep10-nonstop
flan-t5-large-extraction-all-dm_4000-ep10-nonstop is a machine learning model from Zekunli. 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
- Apr 17, 2023
- Updated
- Apr 17, 2023
- SHA
- 3a06058c
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. -->
flan-t5-large-extraction-all-dm_4000-ep10-nonstop
This model is a fine-tuned version of google/flan-t5-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5811
- Hint Hit Num: 2.366
- Hint Precision: 0.4373
- Num: 5.3574
- Gen Len: 18.7764
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: 12
- eval_batch_size: 96
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Hint Hit Num | Hint Precision | Num | Gen Len |
|---|---|---|---|---|---|---|---|
| 2.2334 | 0.6 | 200 | 1.5968 | 2.7154 | 0.4867 | 5.6214 | 18.96 |
| 2.0104 | 1.2 | 400 | 1.5582 | 2.6402 | 0.4732 | 5.5972 | 18.9258 |
| 1.8919 | 1.8 | 600 | 1.5531 | 2.5878 | 0.4689 | 5.5198 | 18.8178 |
| 1.8495 | 2.4 | 800 | 1.5572 | 2.4834 | 0.4556 | 5.4372 | 18.8354 |
| 1.7984 | 2.99 | 1000 | 1.5569 | 2.426 | 0.4514 | 5.352 | 18.791 |
| 1.7446 | 3.59 | 1200 | 1.5443 | 2.4482 | 0.4502 | 5.4084 | 18.8278 |
| 1.7167 | 4.19 | 1400 | 1.5532 | 2.4502 | 0.4524 | 5.3864 | 18.8066 |
| 1.6838 | 4.79 | 1600 | 1.5603 | 2.3258 | 0.4387 | 5.2672 | 18.7164 |
| 1.6713 | 5.39 | 1800 | 1.5602 | 2.3874 | 0.4431 | 5.3434 | 18.7818 |
| 1.6403 | 5.99 | 2000 | 1.5636 | 2.313 | 0.4315 | 5.3092 | 18.7958 |
| 1.5963 | 6.59 | 2200 | 1.5745 | 2.353 | 0.4389 | 5.3184 | 18.7622 |
| 1.6203 | 7.19 | 2400 | 1.5757 | 2.3634 | 0.4348 | 5.3772 | 18.784 |
| 1.5808 | 7.78 | 2600 | 1.5822 | 2.33 | 0.4318 | 5.3452 | 18.8062 |
| 1.5436 | 8.38 | 2800 | 1.5791 | 2.3764 | 0.4394 | 5.3548 | 18.7792 |
| 1.5707 | 8.98 | 3000 | 1.5786 | 2.3592 | 0.4349 | 5.37 | 18.807 |
| 1.5512 | 9.58 | 3200 | 1.5820 | 2.362 | 0.4378 | 5.3418 | 18.773 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.5.1
- Tokenizers 0.12.1
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Apr 17, 2023
- Updated
- Apr 17, 2023