Custom NER Model Development with spaCy

Overview

In this project, I developed a custom Named Entity Recognition (NER) model using spaCy. The process involved manually annotating data, training the model, and evaluating its performance on unseen text. This project provided hands-on experience in working with NLP models, data annotation, and model training pipelines.

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Goals and Skills Learned

• Manual Data Annotation: I manually annotated a dataset with entity labels such as NAME, ROLE, and SKILL. This skill improved my understanding of how to prepare data for machine learning tasks.

• spaCy Custom NER Model Development: Using spaCy, I built a custom NER model, incorporating a structured approach to create and refine entity recognition capabilities.

• Model Training and Evaluation: I trained the model using spaCy's built-in training functionality and evaluated its performance using custom evaluation datasets.

• Error Handling and Debugging: Throughout the process, I encountered and resolved various issues related to annotation structure, data preprocessing, and model training.

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Steps Taken

1. Manual Data Annotation:

   • Annotated a dataset with entities for NAME, ROLE, and SKILL.
   • Used JSON format for annotations, ensuring each entity had a corresponding start and end index.

2. Data Preparation:

   • Loaded annotations from a JSON file and parsed the data to extract texts and their corresponding entities.

3. spaCy Model Training:

   • Initialized a blank spaCy model (en_core_web_sm) and added custom NER capabilities.
   • Added annotations by processing each text and mapping entities to their respective spans and labels.

4. Training Pipeline:

   • Applied training to the custom NER model using spaCy’s update method and training examples.
   • Evaluated the model using custom evaluation data to ensure accuracy and performance.

5. Evaluation:

   • Used spaCy’s built-in evaluation methods to measure precision, recall, and F1-score.
   • Achieved a perfect 100% accuracy in metrics for the custom NER model.

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Application to Resume Project

• Resume Data Extraction:
  • The custom NER model will be integrated into my resume project to automatically extract and categorize relevant entities such as names, roles, and skills from resumes.
  • This will streamline the process of building a comprehensive resume database and enhance the search and filtering capabilities for roles and skill sets.

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Challenges and Solutions

• Error Handling:

  • Overcame issues such as incorrect entity formats, ensuring that only integer indices were used for entity spans.
  • Corrected issues related to model training inputs being incorrectly structured or parsed.

• Debugging:

  • Worked through errors in model training pipeline, refining entity handling and updating process using examples.

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Future Work

• Fine-tuning: Further refining the model by adding more data and iteratively improving performance.
• Deployment: Using the model in a real-world application for tasks such as extracting roles, skills, and responsibilities from text data.

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Conclusion

This project provided a comprehensive approach to building a custom NER model, honing skills in data annotation, NLP model development, and model evaluation.