jobbert-de

JobBERT-de (jobbert-de-v2)

A German job-title → ESCO occupation classifier: fine-tuned deepset/gbert-base with a closed 135-way sequence-classification head over the ESCO occupation scope of the Stellen-Atlas gold set. Given a German job title (and optional short text), it predicts the most likely ESCO occupation URI.

Part of the Stellen-Atlas project — an open German/DACH jobs + skills corpus, model, and demo.

Intended use

• Input: a German job title (the production text is title + derived description; the corpus carries titles only, so titles dominate the signal).
• Output: one of 135 ESCO occupation URIs (config.id2label), argmax over the head.
• Use cases: occupation tagging / normalization of German vacancy titles, labour-market analytics, mapping postings onto the ESCO taxonomy (and via ESCO→ISCO onward to KldB).

Out of scope

• Occupation only. It does not predict skills or the green-job flag — those are not title-checkable and were never gold-annotated (see the dataset card).
• Closed 135-occupation scope. Any occupation outside the gold scope is unreachable; the model will pick the nearest in-scope class instead. It is not an open-set tagger.
• Not a substitute for human review in high-stakes decisions (hiring, eligibility, pay).

How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

repo = "mischeiwiller/jobbert-de"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo).eval()

title = "Softwareentwickler (m/w/d) Backend"
inputs = tok(title, truncation=True, max_length=64, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
pred_id = int(logits.argmax(-1))
esco_uri = model.config.id2label[pred_id]   # an ESCO occupation URI
print(esco_uri)

Method — why v2 beats v1

The honest two-step story behind this checkpoint:

• v1 (weak-distillation, superseded): a 2,146-way head trained on the corpus's own zero-shot E5 labels. A student distilled from a noisy teacher tied but never beat that teacher on macro-F1 (0.1899 vs the 0.1948 zero-shot baseline). Distilling weak labels caps the student at ~the teacher.
• v2 (LLM-supervised, this checkpoint): instead of class-balancing the same weak labels, the labels themselves were replaced. ~6,000 corpus postings (disjoint from the gold set) were each shown a scope-restricted German candidate menu (zero-shot top-10 ∩ the 135 gold occupations) and labelled to the single best ESCO occupation — or "none" — by Claude (Anthropic), yielding 5,603 scope-valid silver labels over 127 of the 135 classes. deepset/gbert-base was then fine-tuned as a closed 135-way classifier on this silver set.

The LLM-labeller was itself validated against the hand gold labels before the silver run: top-1 agreement 0.70 (vs 0.30 for the zero-shot teacher).

Evaluation

Held-out gold set: gold/gold.parquet — 208 German titles, occupations annotated independently of any model prediction (non-circular). Scored with a dependency-free single-label macro-F1 / top-1 metric (cross-checked against scikit-learn). The zero-shot baseline is intfloat/multilingual-e5-base restricted to the same 135 occupations, so both predict over the identical closed label space (apples-to-apples).

jobbert-de-v2 beats the fairly-scoped zero-shot on both metrics — the LLM-supervised labels let the closed head learn the scope's occupations rather than echo the teacher's mistakes.

The original Phase-9.1 baseline (0.1948 macro-F1 / 0.3029 top-1) ranked zero-shot over the full ~3,000-occupation ESCO space. That is an unfair bar for a closed 135-way classifier, so the table above uses the re-baselined 135-scope numbers.

Training

• Base model: deepset/gbert-base (German BERT).
• Head: 135-class sequence classification (ESCO occupation URIs in config.id2label).
• Data: Claude-labelled silver set (5,379 train / 224 val) over the 135-occupation scope.
• Recipe: 3 epochs, batch 64, max_len 64, lr 5e-5, class-balanced cross-entropy (inverse-frequency weights, clip 10.0) + label smoothing 0.1.
• Validation: macro-F1 0.7034, accuracy 0.7857 (in-distribution silver val).

Limitations

• 127 / 135 scope classes have silver training rows (8 had none), capping recall on those 8 occupations.
• Title-only signal: the corpus has no free-text descriptions, so terse or ambiguous titles are hard; predictions are strict exact-URI matches.
• Single-source bias: training and gold data derive from Bundesagentur für Arbeit vacancies — domain/register skews toward that source.
• Silver labels are LLM-generated, not human-verified at scale (gold-validated at 0.70 agreement, not 1.0).
• ESCO preferred_label text in the corpus is mixed DE/EN; only the URI is the join key.

Provenance & license

• Model license: Apache-2.0 (this fine-tune). Base model deepset/gbert-base is MIT.
• Label taxonomy: ESCO v1.2.0 (© European Union, CC-BY-4.0). Occupation URIs are ESCO conceptUri values.
• Training corpus: mischeiwiller/german-job-postings (derived from Bundesagentur für Arbeit Jobbörse API; see the dataset card for source ToS and the BA KldB non-commercial caveat).
• Silver labels: generated with Claude (Anthropic).

Companion artifacts

Part of the Stellen-Atlas project (dataset + model + demo):

• Dataset: mischeiwiller/german-job-postings — the open German/DACH jobs + skills corpus this model was trained on.
• Space: mischeiwiller/stellen-atlas — interactive demo: paste a German ad → ESCO occupation + skills + green share.

Citation

@misc{stellen_atlas_jobbert_de,
  title  = {JobBERT-de: German job-title to ESCO occupation classifier},
  author = {Scheiwiller, Michael},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/mischeiwiller/jobbert-de}},
  note   = {Part of the Stellen-Atlas project; fine-tuned from deepset/gbert-base on Claude-supervised silver labels.}
}