AP678/job-title-ner

Job Title NER

Fine-tuned from dslim/bert-base-NER to extract a single job title span from a job description.

Label	Meaning
B-JOB_TITLE	First token of the job title
I-JOB_TITLE	Continuation token of the job title
O	Not part of a job title

The model returns an empty sequence when no job title is detectable in the text.

Usage

from transformers import pipeline

pipe = pipeline(
    "ner",
    model="AP678/job-title-ner",
    aggregation_strategy="simple",
)

text = """
We are seeking a Senior Data Engineer to join our platform team.
You will design and maintain our data pipelines at scale.
"""

results = pipe(text)
job_titles = [r for r in results if r["entity_group"] == "JOB_TITLE"]
print(job_titles)
# [{ 'entity_group': 'JOB_TITLE', 'score': 0.99, 'word': 'Senior Data Engineer', ... }]

Training

• Base model: dslim/bert-base-NER
• Dataset: ~137K job descriptions scraped from job boards
• Positives: up to 30 000 examples with a labeled title span
• Negatives: up to 10 000 examples with all-O labels (no title found)
• Label scheme: BIO — O / B-JOB_TITLE / I-JOB_TITLE
• Max sequence length: 384 tokens