MB55/rembert-qlora

ReMBERT QLoRA – Grounding Act Classification

This model is a fine-tuned version of 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

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()