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t5-small-finetuned-NL2BASH-customv3
alexsha/t5-small-finetuned-NL2BASH-customv3
t5-small-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 6, 2023
- Updated
- Mar 6, 2023
- SHA
- 6f7e332d
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-small-finetuned-NL2BASH-customv3
This model is a fine-tuned version of kevinum/t5-small-finetuned-English-to-BASH on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9899
- Nl2bash M: 0.6953
- Gen Len: 13.0909
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: 0.0001
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Nl2bash M | Gen Len |
|---|---|---|---|---|---|
| No log | 1.0 | 121 | 1.3618 | 0.4686 | 12.2562 |
| No log | 2.0 | 242 | 1.1341 | 0.6049 | 12.8967 |
| No log | 3.0 | 363 | 1.0489 | 0.6468 | 13.0909 |
| No log | 4.0 | 484 | 1.0048 | 0.684 | 13.0661 |
| 1.3783 | 5.0 | 605 | 0.9899 | 0.6953 | 13.0909 |
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 6, 2023
- Updated
- Mar 6, 2023