# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("abhishek2244/vehicle-biometric-ner-model")
model = AutoModelForTokenClassification.from_pretrained("abhishek2244/vehicle-biometric-ner-model", device_map="auto")Quick Links
vehicle-biometric-ner-model
This model is a fine-tuned version of ab-ai/pii_model on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0058
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
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: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.951 | 1.0 | 50 | 0.4265 | 0.3947 | 0.3777 | 0.3860 | 0.8781 |
| 0.2273 | 2.0 | 100 | 0.0336 | 0.9611 | 0.9784 | 0.9697 | 0.9939 |
| 0.0272 | 3.0 | 150 | 0.0058 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="abhishek2244/vehicle-biometric-ner-model")