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bert-base-uncased-finetuned-set_5
joetey/bert-base-uncased-finetuned-set_5
bert-base-uncased-finetuned-set_5 is a text classification model from joetey. 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 20, 2025
- Updated
- Feb 20, 2025
- SHA
- ebd83e0d
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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-base-uncased-finetuned-set_5
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3627
- Accuracy: 0.8690
- Qwk: 0.8044
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use 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 | Qwk |
|---|---|---|---|---|---|
| No log | 1.0 | 92 | 0.4092 | 0.8542 | 0.6368 |
| No log | 2.0 | 184 | 0.3892 | 0.8452 | 0.7958 |
| No log | 3.0 | 276 | 0.3484 | 0.8512 | 0.7103 |
| No log | 4.0 | 368 | 0.3624 | 0.8452 | 0.7737 |
| No log | 5.0 | 460 | 0.3627 | 0.8690 | 0.8044 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Feb 20, 2025
- Updated
- Feb 20, 2025