---
license: mit
model-index:
- name: rembert-qlora
  results: []
base_model:
- google/rembert
---

# ReMBERT QLoRA – Grounding Act Classification

This model is a fine-tuned version of [google/rembert](https://huggingface.co/google/rembert), 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 AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("MB55/rembert-qlora")
tokenizer = AutoTokenizer.from_pretrained("MB55/rembert-qlora")

inputs = tokenizer("Also das habe ich jetzt verstanden.", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=1).item()