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:…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy