The model is fine-tuned on the CoNLL2003 dataset, a well-known benchmark for NER. This dataset provides a solid foundation for the model to generalize on general English text.

Example Usage

Below is an example of how to use the model with the Hugging Face Transformers library:

python

from transformers import pipeline

ner = pipeline(task="token-classification", model="IsmaelMousa/modernbert-ner-conll2003", aggregation_strategy="max")

results = ner("Hi, I'm Ismael Mousa from Palestine working for NVIDIA inc.")

for entity in results:
    for key, value in entity.items():
        if key == "entity_group":
            print(f"{entity['word']} => {entity[key]}")

Results:

Ismael Mousa => PER
Palestine => LOC
NVIDIA => ORG

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2306	1.0	1756	0.2243	0.6074	0.6483	0.6272	0.9406
0.1415	2.0	3512	0.1583	0.7258	0.7536	0.7394	0.9583
0.1143	3.0	5268	0.1335	0.7731	0.7989	0.7858	0.9657
0.0913	4.0	7024	0.1145	0.7958	0.8256	0.8104	0.9699
0.0848	5.0	8780	0.1079	0.8120	0.8408	0.8261	0.9720
0.0728	6.0	10536	0.1036	0.8214	0.8452	0.8331	0.9730
0.0623	7.0	12292	0.1032	0.8258	0.8487	0.8371	0.9737
0.0599	8.0	14048	0.0990	0.8289	0.8527	0.8406	0.9745
0.0558	9.0	15804	0.0998	0.8331	0.8541	0.8434	0.9750
0.0559	10.0	17560	0.0992	0.8349	0.8563	0.8455	0.9752

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.2.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

onnx-community/modernbert-ner-conll2003-ONNX

modernbert-ner-conll2003 (ONNX)

Usage with Transformers.js

ModernBERT NER (CoNLL2003)

Model Details

Training Data

Example Usage

Training hyperparameters

Training results

Framework versions

onnx-community/
modernbert-ner-conll2003-ONNX