Open LLM List
bert electrical ner
yasserrmdBERT-ELECTRICAL-NER
Introduction
BERT-ELECTRICAL-NER is a model designed for token classification tasks in the electrical domain. It utilizes the DistilBERT architecture and is trained with the AutoTrain tool on the ElectricalNER dataset.
Architecture
The model is based on the DistilBERT architecture, specifically the distilbert-base-uncased variant. DistilBERT is a smaller, faster, cheaper, and lighter version of BERT, retaining 97% of its language understanding while being 60% faster.
Training
The model was trained using AutoTrain, a tool that simplifies the training process. The training was conducted on the ElectricalNER dataset but details about the training parameters and validation metrics were not provided.
Guide: Running Locally
- Clone the Repository: Clone the BERT-ELECTRICAL-NER repository from Hugging Face.
- Install Dependencies: Install the necessary Python libraries, typically
transformersandtorch. - Load the Model: Use the
transformerslibrary to load the DistilBERT model and tokenizer. - Run Inference: Pass your text data through the model to perform token classification.
For enhanced performance, especially with large datasets, consider using cloud GPUs such as those provided by AWS, Google Cloud, or Azure.
License
The licensing details for BERT-ELECTRICAL-NER are not explicitly mentioned. Users should refer to the repository or contact the author for specific licensing information.
