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