# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("simpliTax/category-v7")
model = AutoModelForSequenceClassification.from_pretrained("simpliTax/category-v7", device_map="auto")Quick Links
category-v7
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: 1.8340
- Accuracy: 0.6194
- Macro F1: 0.1932
- Weighted F1: 0.5556
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 |
|---|---|---|---|---|---|---|
| 3.9298 | 1.0 | 797 | 2.5314 | 0.5184 | 0.1309 | 0.4396 |
| 2.2155 | 2.0 | 1594 | 1.9882 | 0.6024 | 0.1807 | 0.5384 |
| 1.8729 | 3.0 | 2391 | 1.8340 | 0.6194 | 0.1932 | 0.5556 |
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-v7
Base model
simpliTax/bert-automap-pbt-fine-tuned
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="simpliTax/category-v7")