| import gradio as gr |
| from transformers import pipeline |
|
|
| token_skill_classifier = pipeline(model="jjzha/jobbert_skill_extraction", aggregation_strategy="first") |
| token_knowledge_classifier = pipeline(model="jjzha/jobbert_knowledge_extraction", aggregation_strategy="first") |
|
|
|
|
| examples = [ |
| "Knowing Python is a plus", |
| "Recommend changes, develop and implement processes to ensure compliance with IFRS standards", |
| "Experience with Unreal and/or Unity and/or native IOS/Android 3D development and/or Web based 3D engines", |
| ] |
|
|
|
|
| def aggregate_span(results): |
| new_results = [] |
| current_result = results[0] |
|
|
| for result in results[1:]: |
| if result["start"] == current_result["end"] + 1: |
| current_result["word"] += " " + result["word"] |
| current_result["end"] = result["end"] |
| else: |
| new_results.append(current_result) |
| current_result = result |
|
|
| new_results.append(current_result) |
|
|
| return new_results |
|
|
| def ner(text): |
| output_skills = token_skill_classifier(text) |
| for result in output_skills: |
| if result.get("entity_group"): |
| result["entity"] = "Skill" |
| del result["entity_group"] |
|
|
| output_knowledge = token_knowledge_classifier(text) |
| for result in output_knowledge: |
| if result.get("entity_group"): |
| result["entity"] = "Knowledge" |
| del result["entity_group"] |
|
|
| if len(output_skills) > 0: |
| output_skills = aggregate_span(output_skills) |
| if len(output_knowledge) > 0: |
| output_knowledge = aggregate_span(output_knowledge) |
|
|
| return {"text": text, "entities": output_skills}, {"text": text, "entities": output_knowledge} |
|
|
|
|
| demo = gr.Interface(fn=ner, |
| inputs=gr.Textbox(placeholder="Enter sentence here..."), |
| outputs=["highlight", "highlight"], |
| examples=examples) |
|
|
| demo.launch() |
