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RoBERTa-finetuned-privacy-detection
gyr66/RoBERTa-finetuned-privacy-detection
RoBERTa-finetuned-privacy-detection is a token classification model from gyr66. Use it when you need labels on individual words, such as names. 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
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Oct 16, 2023
- Updated
- Oct 18, 2023
- SHA
- 4728148e
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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. -->
RoBERTa-finetuned-privacy-detection
This model is a fine-tuned version of gyr66/RoBERTa-finetuned-privacy-detection on the gyr66/privacy_detection dataset. It achieves the following results on the evaluation set:
- Loss: 0.3534
- Precision: 0.6169
- Recall: 0.7248
- F1: 0.6665
- Accuracy: 0.9061
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: 56
- eval_batch_size: 56
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2027 | 1.0 | 36 | 0.3485 | 0.5913 | 0.7273 | 0.6523 | 0.9030 |
| 0.1652 | 2.0 | 72 | 0.3534 | 0.6153 | 0.7314 | 0.6684 | 0.9053 |
| 0.143 | 3.0 | 108 | 0.3534 | 0.6169 | 0.7248 | 0.6665 | 0.9061 |
Framework versions
- Transformers 4.27.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Languages
- zh
- Created
- Oct 16, 2023
- Updated
- Oct 18, 2023