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bert-base-uncased-finetuned-rte-run_3
karadasrm/bert-base-uncased-finetuned-rte-run_3
bert-base-uncased-finetuned-rte-run_3 is a text classification model from karadasrm. 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
- Apr 5, 2025
- Updated
- Apr 10, 2025
- SHA
- 8a7fcf41
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-rte-run_3
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: 0.6661
- Accuracy: 0.6498
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: 9.77523957760191e-06
- train_batch_size: 32
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 78 | 0.6868 | 0.5812 |
| No log | 2.0 | 156 | 0.6671 | 0.6209 |
| No log | 3.0 | 234 | 0.6999 | 0.5848 |
| No log | 4.0 | 312 | 0.6661 | 0.6498 |
| No log | 5.0 | 390 | 0.6882 | 0.6245 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 5, 2025
- Updated
- Apr 10, 2025