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bert-base-uncased-finetuned-FINAL
BaturalpKorpe/bert-base-uncased-finetuned-FINAL
bert-base-uncased-finetuned-FINAL is a text classification model from BaturalpKorpe. 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
- Dec 5, 2025
- Updated
- Dec 5, 2025
- SHA
- ab85eadd
Try a prompt
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-base-uncased-finetuned-FINAL
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4154
- Accuracy: 0.8505
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: 4.345322081412521e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 58 | 0.4166 | 0.8064 |
| No log | 2.0 | 116 | 0.3793 | 0.8333 |
| No log | 3.0 | 174 | 0.4154 | 0.8505 |
| No log | 4.0 | 232 | 0.5029 | 0.8456 |
| No log | 5.0 | 290 | 0.5511 | 0.8456 |
Framework versions
- Transformers 4.57.2
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 5, 2025
- Updated
- Dec 5, 2025