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t5-large-finetuned-NL2BASH-customv3
alexsha/t5-large-finetuned-NL2BASH-customv3
t5-large-finetuned-NL2BASH-customv3 is a machine learning model from alexsha. 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 8, 2023
- Updated
- Mar 9, 2023
- SHA
- bc4fac10
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-finetuned-NL2BASH-customv3
This model is a fine-tuned version of alexsha/t5-large-finetuned-English-to-BASH on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7159
- Nl2bash M: 0.8822
- Gen Len: 13.4256
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: 3e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Nl2bash M | Gen Len |
|---|---|---|---|---|---|
| No log | 1.0 | 387 | 0.7666 | 0.8897 | 13.2314 |
| 0.0177 | 2.0 | 774 | 0.7878 | 0.887 | 13.3512 |
| 0.0188 | 3.0 | 1161 | 0.7580 | 0.8872 | 13.5661 |
| 0.0461 | 4.0 | 1548 | 0.7121 | 0.8846 | 13.3926 |
| 0.0461 | 5.0 | 1935 | 0.7159 | 0.8822 | 13.4256 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.6.1
- Tokenizers 0.11.0
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Mar 8, 2023
- Updated
- Mar 9, 2023