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bert_en_task-A
BenjaminOcampo/bert_en_task-A
bert_en_task-A is a text classification model from BenjaminOcampo. 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
- Nov 29, 2025
- Updated
- Nov 29, 2025
- SHA
- a1a44bde
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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_en_task-A
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: 0.3077
- Accuracy: 0.9221
- Precision: 0.4610
- Recall: 0.5
- F1: 0.4797
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: 5e-05
- train_batch_size: 8
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.3612 | 1.0 | 90 | 0.2737 | 0.9221 | 0.4610 | 0.5 | 0.4797 |
| 0.3527 | 2.0 | 180 | 0.2659 | 0.9221 | 0.4610 | 0.5 | 0.4797 |
| 0.2968 | 3.0 | 270 | 0.3077 | 0.9221 | 0.4610 | 0.5 | 0.4797 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1
- Datasets 4.4.1
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 29, 2025
- Updated
- Nov 29, 2025