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gelectra-base-germanquad-finetuned-squad
princetyagi/gelectra-base-germanquad-finetuned-squad
gelectra-base-germanquad-finetuned-squad is a question answering model from princetyagi. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Question Answering
- Library
- transformers
- License
- mit
- Model type
- electra
- Access
- Public
- Created
- Mar 18, 2023
- Updated
- Mar 21, 2023
- SHA
- 51f292db
Try a prompt
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. -->
gelectra-base-germanquad-finetuned-squad
This model is a fine-tuned version of deepset/gelectra-base-germanquad on the germanquad dataset. It achieves the following results on the evaluation set:
- Loss: 2.5011
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 180 | 1.7745 |
| No log | 2.0 | 360 | 1.8828 |
| 1.0639 | 3.0 | 540 | 1.9312 |
| 1.0639 | 4.0 | 720 | 2.1928 |
| 1.0639 | 5.0 | 900 | 2.2980 |
| 0.6008 | 6.0 | 1080 | 2.3243 |
| 0.6008 | 7.0 | 1260 | 2.3856 |
| 0.6008 | 8.0 | 1440 | 2.4762 |
| 0.4335 | 9.0 | 1620 | 2.4866 |
| 0.4335 | 10.0 | 1800 | 2.5011 |
Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Question Answering
- Library
- transformers
- Type
- electra
- License
- mit
- Created
- Mar 18, 2023
- Updated
- Mar 21, 2023