Instructions to use simpliTax/category-v7-weighted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simpliTax/category-v7-weighted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="simpliTax/category-v7-weighted")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("simpliTax/category-v7-weighted") model = AutoModelForSequenceClassification.from_pretrained("simpliTax/category-v7-weighted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
category-v7-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: 2.5383
- Accuracy: 0.6081
- Macro F1: 0.2318
- Weighted F1: 0.5579
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 |
|---|---|---|---|---|---|---|
| 4.7055 | 1.0 | 797 | 3.4246 | 0.4901 | 0.1518 | 0.4226 |
| 3.0614 | 2.0 | 1594 | 2.7407 | 0.5918 | 0.2181 | 0.5384 |
| 2.6822 | 3.0 | 2391 | 2.5383 | 0.6081 | 0.2318 | 0.5579 |
Framework versions
- Transformers 5.0.0.dev0
- Pytorch 2.9.0+cu126
- Datasets 4.3.0
- Tokenizers 0.22.1
- Downloads last month
- 18
Model tree for simpliTax/category-v7-weighted
Base model
simpliTax/bert-automap-pbt-fine-tuned