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IceBERT-finetuned-squad-10
gudjonk93/IceBERT-finetuned-squad-10
IceBERT-finetuned-squad-10 is a question answering model from gudjonk93. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as agpl-3.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Question Answering
- Library
- transformers
- License
- agpl-3.0
- Model type
- roberta
- Access
- Public
- Created
- Feb 5, 2023
- Updated
- Feb 5, 2023
- SHA
- 20950cd2
Try a prompt
Datasets
Spaces
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. -->
IceBERT-finetuned-squad-10
This model is a fine-tuned version of mideind/IceBERT on the icelandic-qa-n_qi_i dataset. It achieves the following results on the evaluation set:
- Loss: 2.1511
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 | 293 | 1.7614 |
| 1.9509 | 2.0 | 586 | 1.5208 |
| 1.9509 | 3.0 | 879 | 1.5011 |
| 0.9529 | 4.0 | 1172 | 1.5694 |
| 0.9529 | 5.0 | 1465 | 1.7516 |
| 0.6647 | 6.0 | 1758 | 1.8629 |
| 0.4336 | 7.0 | 2051 | 1.8881 |
| 0.4336 | 8.0 | 2344 | 2.0768 |
| 0.335 | 9.0 | 2637 | 2.1238 |
| 0.335 | 10.0 | 2930 | 2.1511 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Question Answering
- Library
- transformers
- Type
- roberta
- License
- agpl-3.0
- Created
- Feb 5, 2023
- Updated
- Feb 5, 2023