# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Risheeb/Docket_Classification_NER")
model = AutoModelForTokenClassification.from_pretrained("Risheeb/Docket_Classification_NER", device_map="auto")Quick Links
Docket_Classification_NER
This model is a fine-tuned version of answerdotai/ModernBERT-large 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for Risheeb/Docket_Classification_NER
Base model
answerdotai/ModernBERT-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Risheeb/Docket_Classification_NER")