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flan-t5-base-squad2-swe
Gabriel/flan-t5-base-squad2-swe
flan-t5-base-squad2-swe is a machine learning model from Gabriel. 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
- Dec 30, 2022
- Updated
- Dec 31, 2022
- SHA
- 2bd69a00
Datasets
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-squad-swe2
This model is a fine-tuned version of google/flan-t5-base on the squad_v2_sv dataset. It achieves the following results on the evaluation set:
- Loss: 1.4248
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|---|---|---|---|
| 2.0881 | 1.0 | 890 | 1.6422 |
| 1.7772 | 2.0 | 1780 | 1.5586 |
| 1.6763 | 3.0 | 2670 | 1.5153 |
| 1.6215 | 4.0 | 3560 | 1.4852 |
| 1.5912 | 5.0 | 4450 | 1.4629 |
| 1.5651 | 6.0 | 5340 | 1.4481 |
| 1.5407 | 7.0 | 6230 | 1.4374 |
| 1.5278 | 8.0 | 7120 | 1.4308 |
| 1.5137 | 9.0 | 8010 | 1.4269 |
| 1.5116 | 10.0 | 8900 | 1.4248 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Dec 30, 2022
- Updated
- Dec 31, 2022