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bert_model_out
whitesong/bert_model_out
bert_model_out is a text classification model from whitesong. 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
- Feb 17, 2025
- Updated
- Feb 17, 2025
- SHA
- 79177570
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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_model_out
This model is a fine-tuned version of beomi/kcbert-base on the unsmile_data dataset. It achieves the following results on the evaluation set:
- Loss: 0.1615
- Lrap: 0.8736
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Lrap |
|---|---|---|---|---|
| No log | 1.0 | 235 | 0.1218 | 0.8759 |
| No log | 2.0 | 470 | 0.1313 | 0.8744 |
| 0.0721 | 3.0 | 705 | 0.1407 | 0.8759 |
| 0.0721 | 4.0 | 940 | 0.1519 | 0.8733 |
| 0.0356 | 5.0 | 1175 | 0.1542 | 0.8760 |
| 0.0356 | 6.0 | 1410 | 0.1613 | 0.8741 |
| 0.0203 | 7.0 | 1645 | 0.1615 | 0.8736 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Feb 17, 2025
- Updated
- Feb 17, 2025