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bert-base-cased-finetuned-swag
Rocketknight1/bert-base-cased-finetuned-swag
bert-base-cased-finetuned-swag is a multiple choice model from Rocketknight1. Use it for the multiple choice 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.
probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- multiple choice
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jul 21, 2022
- SHA
- f23b93a8
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
Rocketknight1/bert-base-cased-finetuned-swag
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.8709
- Train Accuracy: 0.6465
- Validation Loss: 0.6167
- Validation Accuracy: 0.7590
- Epoch: 0
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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 9192, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.8709 | 0.6465 | 0.6167 | 0.7590 | 0 |
Framework versions
- Transformers 4.21.0.dev0
- TensorFlow 2.9.1
- Datasets 2.3.3.dev0
- Tokenizers 0.11.0
- Task
- multiple choice
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Jul 21, 2022