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import gradio as gr |
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import spaces |
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from transformers import pipeline |
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token_skill_classifier = pipeline(model="jjzha/escoxlmr_skill_extraction", aggregation_strategy="first", device=0) |
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token_knowledge_classifier = pipeline(model="jjzha/escoxlmr_knowledge_extraction", aggregation_strategy="first", device=0) |
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examples = [ |
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"Knowing Python is a plus", |
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"Je hebt aantoonbaar ervaring met sleutelen aan fietsen", |
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"Du har en relevant datavidenskabelig, matematisk, økonomisk, ingeniør- eller it-mæssig baggrund", |
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"Du besitzt einen Führerschein der Klasse B", |
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"Vous aimez les projets de grande envergure et vous savez traiter des données en grande quantité", |
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"Per avere successo in questo ruolo, dovrai avere una forte motivazione, una grande determinazione e non necessariamente un'esperienza nel settore." |
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] |
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def aggregate_span(results): |
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new_results = [] |
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current_result = results[0] |
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for result in results[1:]: |
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if result["start"] == current_result["end"] + 1: |
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current_result["word"] += " " + result["word"] |
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current_result["end"] = result["end"] |
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else: |
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new_results.append(current_result) |
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current_result = result |
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new_results.append(current_result) |
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return new_results |
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@spaces.GPU |
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def ner(text): |
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output_skills = token_skill_classifier(text) |
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for result in output_skills: |
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if result.get("entity_group"): |
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result["entity"] = "Skill" |
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del result["entity_group"] |
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output_knowledge = token_knowledge_classifier(text) |
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for result in output_knowledge: |
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if result.get("entity_group"): |
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result["entity"] = "Knowledge" |
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del result["entity_group"] |
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if len(output_skills) > 0: |
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output_skills = aggregate_span(output_skills) |
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if len(output_knowledge) > 0: |
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output_knowledge = aggregate_span(output_knowledge) |
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return {"text": text, "entities": output_skills}, {"text": text, "entities": output_knowledge} |
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demo = gr.Interface(fn=ner, |
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inputs=gr.Textbox(placeholder="Enter sentence here..."), |
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outputs=["highlight", "highlight"], |
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examples=examples) |
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demo.launch() |
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