MB55/gbert-lora-final

Gbert QLoRA – Grounding Act Classification

This model is a fine-tuned version of 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

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)