| --- |
| language: |
| - en |
| - fr |
| - de |
| - es |
| - it |
| license: apache-2.0 |
| tags: |
| - gliner |
| - gliner2 |
| - text-classification |
| - multi-label |
| - hr |
| - human-resources |
| - multilingual |
| pipeline_tag: token-classification |
| base_model: urchade/gliner_large-v2.1 |
| --- |
| |
| # Gliner2HR — Multi-Label HR Conversation Classifier |
|
|
| **Gliner2HR** is a fine-tuned [GLiNER2-large](https://huggingface.co/urchade/gliner_large-v2.1) model for **multi-label classification of HR workplace conversations** across 18 intent categories. It supports English, French, German, Spanish, and Italian. |
|
|
| ## Benchmark |
|
|
| Evaluated on [xwjzds/hr_multiwoz_tod_sgd](https://huggingface.co/datasets/xwjzds/hr_multiwoz_tod_sgd) — 550 single-label HR dialogues, 8 active categories, single-label classification task: |
|
|
| | Model | Accuracy | Macro F1 | Precision | Recall | |
| |---|---|---|---|---| |
| | **Gliner2HR (fine-tuned)** | **69.3%** | **0.807** | **0.954** | 0.716 | |
| | GLiNER2-large (base, no fine-tuning) | 42.9% | 0.535 | 0.728 | 0.459 | |
|
|
| GLiHR outperforms the untuned base model by **+26.4 points accuracy** and **+27.1 points Macro F1** at threshold 0.3. |
|
|
| Per-label F1 on benchmark: |
|
|
| | Label | GLiHR | Base | Delta | |
| |---|---|---|---| |
| | Harassment | 0.925 | 0.800 | +0.125 | |
| | Leave & Absence | 0.892 | 0.787 | +0.105 | |
| | Benefits | 0.868 | 0.764 | +0.104 | |
| | Mobility | 0.838 | 0.000 | +0.838 | |
| | Training & Development | 0.807 | 0.563 | +0.244 | |
| | Health & Safety | 0.817 | 0.682 | +0.135 | |
| | Performance Management | 0.775 | 0.206 | +0.569 | |
| | IT & Equipment | 0.530 | 0.478 | +0.052 | |
|
|
| ## Supported Labels (18 categories) |
|
|
| `Benefits` · `Compliance & Legal` · `Contracts` · `DEI` · `Expense Management` · `Harassment` · `Health & Safety` · `IT & Equipment` · `Leave & Absence` · `Mobility` · `Offboarding` · `Onboarding` · `Payroll` · `Performance Management` · `Recruitment` · `Timetracking` · `Training & Development` · `Work Arrangements` |
|
|
| ## Usage |
|
|
| ```python |
| from gliner import GLiNER |
| |
| model = GLiNER.from_pretrained("AurelPx/gliner2hr") |
| |
| labels = [ |
| "Benefits", "Compliance & Legal", "Contracts", "DEI", "Expense Management", |
| "Harassment", "Health & Safety", "IT & Equipment", "Leave & Absence", "Mobility", |
| "Offboarding", "Onboarding", "Payroll", "Performance Management", "Recruitment", |
| "Timetracking", "Training & Development", "Work Arrangements" |
| ] |
| |
| text = """USER: I have not received my payslip for March yet. |
| AGENT: I checked the payroll system, it was generated on the 28th. |
| USER: Also, I would like to understand my pension contributions.""" |
| |
| entities = model.predict_entities(text, labels, threshold=0.3) |
| predicted_labels = list({e["label"] for e in entities}) |
| # -> ["Payroll", "Benefits"] |
| ``` |
|
|
| ## Recommended Threshold |
|
|
| - `0.3` — best accuracy (69.3%) and F1 (0.807), recommended for most use cases |
| - `0.5` — slightly lower recall, higher precision (P=0.954 at 0.3 already very high) |
|
|
| ## Training Data |
|
|
| Fine-tuned on **2,559 synthetic multi-turn HR conversations** (2–5 turns, `USER: ... AGENT: ...` format) covering all 18 categories, generated in English, French, German, Spanish, and Italian. Labels were assigned during generation — no post-hoc keyword matching. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{gliner2hr, |
| title = {Gliner2HR: Multi-Label HR Conversation Classification with GLiNER2}, |
| year = {2025}, |
| note = {Fine-tuned GLiNER2-large on synthetic multilingual HR dialogues. https://huggingface.co/AurelPx/glihr} |
| } |
| ``` |
|
|
