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bert-base-uncased-sst2-membership-attack
doyoungkim/bert-base-uncased-sst2-membership-attack
bert-base-uncased-sst2-membership-attack is a text classification model from doyoungkim. Use it when you need a label for a piece of text. 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
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Sep 12, 2021
- SHA
- a879ec2c
Try a prompt
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-sst2-membership-attack
This model is a fine-tuned version of bert-base-uncased on an unkown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6296
- Accuracy: 0.8681
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6921 | 1.0 | 3813 | 0.6263 | 0.8360 |
| 0.6916 | 2.0 | 7626 | 0.6296 | 0.8681 |
| 0.6904 | 3.0 | 11439 | 0.6105 | 0.8406 |
| 0.6886 | 4.0 | 15252 | 0.6192 | 0.8200 |
| 0.6845 | 5.0 | 19065 | 0.6250 | 0.7798 |
Framework versions
- Transformers 4.9.2
- Pytorch 1.8.1
- Datasets 1.11.0
- Tokenizers 0.10.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Sep 12, 2021