onnx-community/modernbert-ner-conll2003-ONNX

modernbert-ner-conll2003 (ONNX)

This is an ONNX version of IsmaelMousa/modernbert-ner-conll2003. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js

See the pipeline documentation for token-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TokenClassificationPipeline

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ModernBERT NER (CoNLL2003)

This model is a fine-tuned version of answerdotai/ModernBERT-base on the conll2003 dataset for Named Entity Recognition (NER).

Robust performance on tasks involving the recognition of Persons, Organizations, and Locations.

It achieves the following results on the evaluation set:

• Loss: 0.0992
• Precision: 0.8349
• Recall: 0.8563
• F1: 0.8455
• Accuracy: 0.9752

Model Details

• Base Model: ModernBERT: https://doi.org/10.48550/arXiv.2412.13663.
• Fine-tuning Dataset: CoNLL2003: https://huggingface.co/datasets/eriktks/conll2003.
• Task: Named Entity Recognition (NER)

Training Data

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:

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