---
license: mit
base_model:
- deepset/gbert-large
---

# Gbert QLoRA – Grounding Act Classification

This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large), optimized using QLoRA for efficient binary classification of German dialogue utterances into:

- `ADVANCE`: Contribution that moves the dialogue forward (e.g. confirmations, follow-ups, elaborations)
- `NON-ADVANCE`: Other utterances (e.g. vague responses, misunderstandings, irrelevant comments)

## Use Cases

- Dialogue system analysis
- Teacher-student interaction classification
- Grounding in institutional advising or classroom discourse



## How to Use

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("MB55/gbert-lora-final")
tokenizer = AutoTokenizer.from_pretrained("MB55/gbert-lora-final")

text = "Bitte erläutern Sie das noch einmal."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

predicted_class = outputs.logits.argmax(dim=-1).item()
print(predicted_class)