bert base arabic camelbert mix ner

Introduction

The CAMeLBERT-Mix NER Model is a Named Entity Recognition (NER) model tailored for the Arabic language. It is based on the CAMeLBERT Mix model and fine-tuned using the ANERcorp dataset. Detailed information on the model's fine-tuning process and hyperparameters can be found in the associated research paper.

Architecture

The model uses the BERT architecture, specifically adapted for the Arabic language. It is designed for token classification tasks and is compatible with both PyTorch and TensorFlow frameworks.

Training

The CAMeLBERT-Mix NER Model was fine-tuned using the ANERcorp dataset. The training process and hyperparameters are documented in the paper titled "The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models." The fine-tuning code is accessible on GitHub.

Guide: Running Locally

To use the CAMeLBERT-Mix NER model locally, follow these steps:

1. Install Dependencies: Ensure you have Python and the necessary libraries, including transformers version 3.5.0 or higher.
2. Download Model: Use the transformers library to load the model:

   from transformers import pipeline
   ner = pipeline('ner', model='CAMeL-Lab/bert-base-arabic-camelbert-mix-ner')
   

3. Predict: Pass Arabic text to the model for NER predictions:

   ner("إمارة أبوظبي هي إحدى إمارات دولة الإمارات العربية المتحدة السبع")
   

4. Optional Tools: Utilize the CAMeL Tools NER component for additional functionality.

For optimal performance, consider using cloud GPUs from providers like AWS or Google Cloud to handle computational demands, especially for large datasets.

License

The CAMeLBERT-Mix NER Model is released under the Apache-2.0 License, allowing for free use and distribution with proper attribution.