# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Virus-Proton/PII_NER")
model = AutoModelForTokenClassification.from_pretrained("Virus-Proton/PII_NER")Quick Links
PII_NER
This model is a fine-tuned version of lakshyakh93/deberta_finetuned_pii on the None dataset.
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
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Model tree for Virus-Proton/PII_NER
Base model
lakshyakh93/deberta_finetuned_pii
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Virus-Proton/PII_NER")