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bert-tiny-sentiment
wchang778/bert-tiny-sentiment
bert-tiny-sentiment is a text classification model from wchang778. Use it when you need a label for a piece of text. 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
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jul 6, 2026
- Updated
- Jul 6, 2026
- SHA
- 63b10a42
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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-tiny-sentiment
This model is a fine-tuned version of prajjwal1/bert-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4232
- Accuracy: 0.842
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6127 | 1.0 | 500 | 0.5195 | 0.749 |
| 0.4343 | 2.0 | 1000 | 0.4183 | 0.806 |
| 0.3443 | 3.0 | 1500 | 0.4118 | 0.819 |
| 0.3037 | 4.0 | 2000 | 0.3967 | 0.843 |
| 0.2676 | 5.0 | 2500 | 0.4043 | 0.844 |
| 0.2467 | 6.0 | 3000 | 0.4232 | 0.842 |
Framework versions
- Transformers 5.13.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jul 6, 2026
- Updated
- Jul 6, 2026