Downloads · 30 days
15
19% of all-time downloads
bert-base-uncased-finetuned-swag
Benlitzen43/bert-base-uncased-finetuned-swag
bert-base-uncased-finetuned-swag is a multiple choice model from Benlitzen43. 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. --
All-time downloads
78
Public
Repo size
38.1 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin438 MB · 100%
At a glance
- Task
- multiple choice
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Nov 22, 2023
- Updated
- Nov 22, 2023
- SHA
- 1fcd706e
Base models
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. -->
bert-base-uncased-finetuned-swag
This model is a fine-tuned version of bert-base-uncased on the cosmos_qa dataset. It achieves the following results on the evaluation set:
- Loss: 3.2446
- Accuracy: 0.5564
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0929 | 1.0 | 6316 | 1.0877 | 0.5404 |
| 0.6544 | 2.0 | 12632 | 1.4658 | 0.5719 |
| 0.3397 | 3.0 | 18948 | 3.2446 | 0.5564 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
- Task
- multiple choice
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 22, 2023
- Updated
- Nov 22, 2023