Instructions to use simpliTax/category-v9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simpliTax/category-v9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="simpliTax/category-v9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("simpliTax/category-v9") model = AutoModelForSequenceClassification.from_pretrained("simpliTax/category-v9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
category-v9
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: 1.9912
- Accuracy: 0.5912
- Macro F1: 0.1862
- Weighted F1: 0.5219
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 |
|---|---|---|---|---|---|---|
| 3.9677 | 1.0 | 738 | 2.7093 | 0.4737 | 0.1143 | 0.3833 |
| 2.7378 | 2.0 | 1476 | 2.1501 | 0.5706 | 0.1695 | 0.4986 |
| 1.9325 | 3.0 | 2214 | 1.9912 | 0.5912 | 0.1862 | 0.5219 |
Framework versions
- Transformers 5.0.0.dev0
- Pytorch 2.9.0+cu126
- Datasets 4.3.0
- Tokenizers 0.22.1
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Model tree for simpliTax/category-v9
Base model
simpliTax/bert-automap-pbt-fine-tuned