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category-v6-strict-weighted
simpliTax/category-v6-strict-weighted
category-v6-strict-weighted is a text classification model from simpliTax. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026
- SHA
- d0397b20
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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. -->
category-v6-strict-weighted
This model is a fine-tuned version of simpliTax/bert-automap-pbt-fine-tuned on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.8059
- Accuracy: 0.6083
- Macro F1: 0.2456
- Weighted F1: 0.5522
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: 13
- optimizer: Use 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 | Macro F1 | Weighted F1 |
|---|---|---|---|---|---|---|
| 4.6842 | 1.0 | 730 | 3.6174 | 0.4757 | 0.1501 | 0.3969 |
| 3.5613 | 2.0 | 1460 | 2.9848 | 0.5883 | 0.2277 | 0.5293 |
| 2.6805 | 3.0 | 2190 | 2.8059 | 0.6083 | 0.2456 | 0.5522 |
Framework versions
- Transformers 5.0.0.dev0
- Pytorch 2.9.0+cu126
- Datasets 4.3.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Aug 12, 2026
- Updated
- Aug 12, 2026