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flan-t5-base
tagoresrisaigalla/flan-t5-base
flan-t5-base is a machine learning model from tagoresrisaigalla. 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
- Feb 15, 2025
- Updated
- Feb 15, 2025
- SHA
- 8a6124c5
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. -->
flan-t5-base
This model is a fine-tuned version of google/flan-t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1442
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 10.7012 | 1.0 | 18 | 0.2887 |
| 0.2143 | 2.0 | 36 | 0.1380 |
| 0.087 | 3.0 | 54 | 0.1287 |
| 0.0641 | 4.0 | 72 | 0.0977 |
| 0.0281 | 5.0 | 90 | 0.1312 |
| 0.0125 | 6.0 | 108 | 0.1415 |
| 0.015 | 7.0 | 126 | 0.1289 |
| 0.0075 | 8.0 | 144 | 0.1352 |
| 0.0055 | 9.0 | 162 | 0.1426 |
| 0.0043 | 10.0 | 180 | 0.1442 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Feb 15, 2025
- Updated
- Feb 15, 2025