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bert-base-newslies
morinaa/bert-base-newslies
bert-base-newslies is a text classification model from morinaa. 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
- Mar 27, 2026
- Updated
- Mar 27, 2026
- SHA
- 74b4abb5
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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-newslies
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: 1.3441
- Accuracy: 0.6710
- F1 Macro: 0.6604
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: 32
- eval_batch_size: 8
- 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 | F1 Macro |
|---|---|---|---|---|---|
| 0.4644 | 1.0 | 320 | 0.6203 | 0.7085 | 0.6891 |
| 0.3512 | 2.0 | 640 | 0.6598 | 0.7018 | 0.6811 |
| 0.2396 | 3.0 | 960 | 0.8541 | 0.6874 | 0.6750 |
| 0.1576 | 4.0 | 1280 | 1.1863 | 0.6710 | 0.6610 |
| 0.1139 | 5.0 | 1600 | 1.3441 | 0.6710 | 0.6604 |
Framework versions
- Transformers 5.4.0
- Pytorch 2.8.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 27, 2026
- Updated
- Mar 27, 2026