Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- accuracy
+- precision
+- f1
+- recall
+pipeline_tag: token-classification
+library_name: spacy
+tags:
+- spacy
+- nlp
+- python
+- skill-extraction
+- ner
+---
+
+# Skill Extraction Model using spaCy
+
+This is a custom **Named Entity Recognition (NER)** model built with **spaCy** to identify and extract skills from resumes and job descriptions.
+
+## Why This Model?
+
+To improve flexibility and accuracy, we transitioned from a static skill extraction approach to a dynamic one. This new method leverages spaCy to fine-tune a pre-trained Named Entity Recognition (NER) model, enabling the extraction of skills directly from resumes and job descriptions. By removing the dependency on predefined skill lists, the model can recognize context-specific, domain-relevant, and even newly emerging skills. This dynamic strategy offers a more adaptive and scalable solution for real-world skill extraction and talent-matching applications.
+
+---
+
+## How to Use
+
+### 1. Load the Model from Hugging Face
+
+```python
+from huggingface_hub import snapshot_download
+import spacy
+
+# Download the model from the Hub
+model_path = snapshot_download("amjad-awad/skill-extractor", repo_type="model")
+
+# Load the model with spaCy
+nlp = spacy.load(model_path)
+
+# Example usage
+text = "Experienced in Python, JavaScript, and cloud services like AWS and Azure."
+doc = nlp(text)
+
+# Extract skill entities
+skills = [ent.text for ent in doc.ents if "SKILLS" in ent.label_]
+print(skills)
+```
+['Python', 'JavaScript', 'cloud', 'AWS', 'Azure']
+```