Instructions to use simpliTax/category-v6-clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simpliTax/category-v6-clean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="simpliTax/category-v6-clean")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("simpliTax/category-v6-clean") model = AutoModelForSequenceClassification.from_pretrained("simpliTax/category-v6-clean", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: mit | |
| base_model: simpliTax/bert-automap-pbt-fine-tuned | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: category-v6-clean | |
| results: [] | |
| <!-- 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-clean | |
| This model is a fine-tuned version of [simpliTax/bert-automap-pbt-fine-tuned](https://huggingface.co/simpliTax/bert-automap-pbt-fine-tuned) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.8516 | |
| - Accuracy: 0.6110 | |
| - Macro F1: 0.1892 | |
| - Weighted F1: 0.5446 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:| | |
| | 3.8879 | 1.0 | 756 | 2.5312 | 0.5112 | 0.1235 | 0.4283 | | |
| | 2.1897 | 2.0 | 1512 | 1.9914 | 0.5842 | 0.1662 | 0.5134 | | |
| | 1.8525 | 3.0 | 2268 | 1.8516 | 0.6110 | 0.1892 | 0.5446 | | |
| ### Framework versions | |
| - Transformers 5.0.0.dev0 | |
| - Pytorch 2.9.0+cu126 | |
| - Datasets 4.3.0 | |
| - Tokenizers 0.22.1 | |