---
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}
}
```