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albert-xxlarge-v2-finetuned-csqa
danlou/albert-xxlarge-v2-finetuned-csqa
albert-xxlarge-v2-finetuned-csqa is a multiple choice model from danlou. 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.
should 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
- albert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jul 23, 2021
- SHA
- 37eddb04
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. -->
albert-xxlarge-v2-finetuned-csqa
This model is a fine-tuned version of albert-xxlarge-v2 on the commonsense_qa dataset. It achieves the following results on the evaluation set:
- Loss: 1.6177
- Accuracy: 0.7871
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: 1e-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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7464 | 1.0 | 609 | 0.5319 | 0.7985 |
| 0.3116 | 2.0 | 1218 | 0.6422 | 0.7936 |
| 0.0769 | 3.0 | 1827 | 1.2674 | 0.7952 |
| 0.0163 | 4.0 | 2436 | 1.4839 | 0.7903 |
| 0.0122 | 5.0 | 3045 | 1.6177 | 0.7871 |
Framework versions
- Transformers 4.8.2
- Pytorch 1.9.0
- Datasets 1.10.2
- Tokenizers 0.10.3
- Task
- multiple choice
- Library
- transformers
- Type
- albert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Jul 23, 2021