electrical ner ModernBERT base EU AI Act Compliance Profile

electrical-ner-ModernBERT-base

Model Description

This model is fine-tuned from answerdotai/ModernBERT-base for token-classification tasks, specifically Named Entity Recognition (NER) in the electrical engineering domain. The model has been optimized to extract entities such as components, materials, standards, and design parameters from technical texts with high precision and recall.

Training Data

The model was trained on the disham993/ElectricalNER dataset, a GPT-4o-mini-generated dataset curated for the electrical engineering domain. This dataset includes diverse technical contexts, such as circuit design, testing, maintenance, installation, troubleshooting, or research.

Model Details

• Base Model: answerdotai/ModernBERT-base
• Task: Token Classification (NER)
• Language: English (en)
• Dataset: disham993/ElectricalNER

Training Procedure

Training Hyperparameters

The model was fine-tuned using the following hyperparameters:

• Evaluation Strategy: epoch
• Learning Rate: 1e-5
• Batch Size: 64 (for both training and evaluation)
• Number of Epochs: 5
• Weight Decay: 0.01

Evaluation Results

The following metrics were achieved during evaluation:

• Precision: 0.9108
• Recall: 0.9248
• F1 Score: 0.9177
• Accuracy: 0.9664
• Evaluation Runtime: 2.121 seconds
• Samples Per Second: 711.447
• Steps Per Second: 11.315

Usage

You can use this model for Named Entity Recognition tasks as follows:

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_name = "disham993/electrical-ner-ModernBERT-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_na

Your obligations as a electrical ner ModernBERT base deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Base Model