A fine-tuned version of numind/NuExtract-tiny-v1.5 (Qwen2.5-0.5B backbone) specialised for resume / CV structured extraction. Given raw resume text in any format, the model returns a clean JSON object with name, contact details, skills, work experience, education, and other details — ready to plug into a hiring pipeline, ATS, or LangChain workflow. Near-zero train/val gap throughout — no overfitting observed. Best checkpoint (step 284, val loss 0.2296) loaded automatically. INVALID LANGUAGE PAIR SPECIFIED. EXAMPLE: LANGPAIR=EN|IT USING 2 LETTER ISO OR RFC3066 LIKE ZH-CN. ALMOST ALL LANGUAGES SUPPORTED BUT SOME MAY HAVE NO CONTENT Skill capitalisation is normalised via .title() during training, so FastAPI may appear as Fastapi in output. Apply a canonical map in post-processing if needed. If you use this model, please also cite the original NuExtract work:
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: nimendraai
Теги: gguf, qwen2, nlp, json, information-extraction, resume-parsing, structured-extraction, unsloth
Лайков: 4 | Загрузок: 204
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
