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bert-large-portuguese-cased_ft-multilple-choice-enem-sample
douglasrolins/bert-large-portuguese-cased_ft-multilple-choice-enem-sample
bert-large-portuguese-cased_ft-multilple-choice-enem-sample is a multiple choice model from douglasrolins. 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 mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- multiple choice
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jan 19, 2024
- Updated
- Jan 19, 2024
- SHA
- 8bfd1d50
Base models
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-large-portuguese-cased_ft-multilple-choice-enem-sample
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.1667
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: 1
- eval_batch_size: 1
- 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 |
|---|---|---|---|---|
| 1.6509 | 1.0 | 1382 | 1.6094 | 0.2210 |
| 1.6376 | 2.0 | 2764 | 1.6094 | 0.1848 |
| 1.6335 | 3.0 | 4146 | 1.6094 | 0.1667 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Task
- multiple choice
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jan 19, 2024
- Updated
- Jan 19, 2024